From 978ddfc9c8f87cbf2cb8c5902aee8eecebd94f35 Mon Sep 17 00:00:00 2001
From: yidong72 <43824965+yidong72@users.noreply.github.com>
Date: Mon, 28 Oct 2019 13:20:52 -0400
Subject: [PATCH 1/6] [REVIEW] Upgrade to RAPIDS 0.10 (#63)

* upgrade to RAPIDS 0.10
* fixed the bug in relative strength
* removed nxpd, fixed the drawing
---
 docker/build.sh                               |  17 +-
 gquant/cuindicator/indicator.py               |   4 +-
 gquant/dataframe_flow/taskGraph.py            |  18 ++
 .../strategy/movingAverageStrategyNode.py     |   8 +-
 .../portExpMovingAverageStrategyNode.py       |   8 +-
 .../strategy/xgboostStrategyNode.py           |   9 +-
 .../plugin_nodes/transform/indicatorNode.py   |  10 +-
 .../transform/returnFeatureNode.py            |   1 +
 notebooks/01_tutorial.ipynb                   |  19 +-
 notebooks/02_single_stock_trade.ipynb         |  12 +-
 notebooks/03_simple_dask_example.ipynb        |  21 +--
 notebooks/04_portfolio_trade.ipynb            |  44 +++--
 notebooks/05_customize_nodes.ipynb            | 163 +++++++++---------
 notebooks/06_xgboost_trade.ipynb              |  37 ++--
 notebooks/07_fractional_differencing.ipynb    | 108 +++++++++---
 .../mortgage_e2e_gquant.ipynb                 |  15 +-
 tests/unit/test_fractional_diff.py            |   4 +-
 tests/unit/test_indicator_node.py             |   2 +-
 tests/unit/test_multi_assets_indicator.py     |   2 +-
 tests/unit/test_rolling.py                    |   2 +-
 tests/unit/test_util.py                       |   2 +-
 21 files changed, 275 insertions(+), 231 deletions(-)

diff --git a/docker/build.sh b/docker/build.sh
index 05feaed8..c3f1fff2 100644
--- a/docker/build.sh
+++ b/docker/build.sh
@@ -38,16 +38,8 @@ case $SYSTEM_CONFIGURATION in
         ;;
 esac
 
-CONTAINER="nvcr.io/nvidia/rapidsai/rapidsai:0.9-cuda${CONTAINER_VER}-runtime-ubuntu${OS_STR}"
+CONTAINER="nvcr.io/nvidia/rapidsai/rapidsai:0.10-cuda${CONTAINER_VER}-runtime-ubuntu${OS_STR}"
 
-read -p "Would you like to install Vim JupyterLab Extension (optional) [N]/y: " VIM_INSTALL
-
-VIM_INSTALL=${VIM_INSTALL:-N}
-if [ "$VIM_INSTALL" = "Y" ] || [ "$VIM_INSTALL" = "y" ]; then
-    echo "Vim JupyterLab Extension will be installed."
-else
-    echo "Vim JupyterLab Extension will not be installed."
-fi
 
 D_FILE=${D_FILE:='Dockerfile.Rapids'}
 D_CONT=${D_CONT:='gquant/gquant:latest'}
@@ -74,7 +66,7 @@ SHELL ["bash","-c"]
 # Additional python libs
 #
 RUN source activate rapids \ 
-    && pip install nxpd $CUPY 
+    && pip install $CUPY
 
 RUN source activate rapids \
     && cd /rapids/gQuant \
@@ -82,7 +74,8 @@ RUN source activate rapids \
 
 RUN source activate rapids \ 
     && conda install -y -c conda-forge dask-labextension recommonmark numpydoc sphinx_rtd_theme pudb \
-    python-graphviz bqplot=0.11.5 nodejs=11.11.0 jupyterlab=0.35.4 ipywidgets=7.4.2 pytables mkl numexpr
+    python-graphviz bqplot=0.11.5 nodejs=11.11.0 jupyterlab=0.35.4 ipywidgets=7.4.2 pytables mkl numexpr \
+    pydot
 
 #
 # required set up
@@ -93,8 +86,6 @@ RUN source activate rapids \
     && mkdir /.local /.jupyter /.config /.cupy  \
     && chmod 777 /.local /.jupyter /.config /.cupy
 
-RUN if [ "$VIM_INSTALL" = "Y" ] || [ "$VIM_INSTALL" = "y" ]; then /conda/envs/rapids/bin/jupyter labextension install jupyterlab_vim@0.10.1 --no-build ; fi
-
 RUN source activate rapids \ 
     && jupyter lab build && jupyter lab clean
 
diff --git a/gquant/cuindicator/indicator.py b/gquant/cuindicator/indicator.py
index bb0412c0..33a497e9 100755
--- a/gquant/cuindicator/indicator.py
+++ b/gquant/cuindicator/indicator.py
@@ -604,10 +604,10 @@ def port_relative_strength_index(asset_indicator, high_arr, low_arr, n):
                           low_arr.data.to_gpu_array())
     UpI_s = shift(UpI, 1)
     UpI_s[0] = 0
-    UpI_s = cudf.Series(UpI_s) * (1.0 - asset_indicator)
+    UpI_s = cudf.Series(UpI_s) * (1.0 - asset_indicator.reset_index(drop=True))
     DoI_s = shift(DoI, 1)
     DoI_s[0] = 0
-    DoI_s = cudf.Series(DoI_s) * (1.0 - asset_indicator)
+    DoI_s = cudf.Series(DoI_s) * (1.0 - asset_indicator.reset_index(drop=True))
     PosDI = PEwm(n, UpI_s, asset_indicator).mean()
     NegDI = PEwm(n, DoI_s, asset_indicator).mean()
     RSI = division(PosDI, summation(PosDI, NegDI))
diff --git a/gquant/dataframe_flow/taskGraph.py b/gquant/dataframe_flow/taskGraph.py
index f4cce62d..b1356e9e 100644
--- a/gquant/dataframe_flow/taskGraph.py
+++ b/gquant/dataframe_flow/taskGraph.py
@@ -269,3 +269,21 @@ def run(self, outputs, replace=None):
         # clean the results afterwards
         output_node.input_df = {}
         return tuple(results)
+
+    def draw(self, show=None, fmt='png'):
+        nx_graph = self.viz_graph()
+        to_pydot = nx.drawing.nx_pydot.to_pydot
+        pdot = to_pydot(nx_graph)
+        pdot_out = pdot.create(format=fmt)
+
+        if show in ('ipynb',):
+            from IPython.display import display
+            if fmt in ('svg',):
+                from IPython.display import SVG as Image
+            else:
+                from IPython.display import Image
+
+            plt = Image(pdot_out)
+            display(plt)
+        else:
+            return pdot_out
diff --git a/gquant/plugin_nodes/strategy/movingAverageStrategyNode.py b/gquant/plugin_nodes/strategy/movingAverageStrategyNode.py
index 0982aa41..3e6b0954 100644
--- a/gquant/plugin_nodes/strategy/movingAverageStrategyNode.py
+++ b/gquant/plugin_nodes/strategy/movingAverageStrategyNode.py
@@ -2,16 +2,17 @@
 from gquant.dataframe_flow import Node
 from numba import cuda
 import math
+import numpy as np
 
 
 @cuda.jit
 def moving_average_signal_kernel(ma_fast, ma_slow, out_arr, arr_len):
     i = cuda.grid(1)
     if i == 0:
-        out_arr[i] = math.inf
+        out_arr[i] = np.nan
     if i < arr_len - 1:
         if math.isnan(ma_slow[i]) or math.isnan(ma_fast[i]):
-            out_arr[i + 1] = math.inf
+            out_arr[i + 1] = np.nan
         elif ma_fast[i] - ma_slow[i] > 0.00001:
             # shift 1 time to make sure no peeking into the future
             out_arr[i + 1] = -1.0
@@ -73,9 +74,10 @@ def process(self, inputs):
         input_df['ma_slow'] = input_df['ma_slow'].fillna(0.0)
         input_df['ma_fast'] = fast
         input_df['ma_fast'] = input_df['ma_fast'].fillna(0.0)
-        input_df = input_df.query('signal<10')  # remove the bad datapints
+        input_df = input_df.dropna()
         return input_df
 
+
 if __name__ == "__main__":
     from gquant.dataloader.csvStockLoader import CsvStockLoader
     from gquant.transform.assetFilterNode import AssetFilterNode
diff --git a/gquant/plugin_nodes/strategy/portExpMovingAverageStrategyNode.py b/gquant/plugin_nodes/strategy/portExpMovingAverageStrategyNode.py
index 751043b2..545468a6 100644
--- a/gquant/plugin_nodes/strategy/portExpMovingAverageStrategyNode.py
+++ b/gquant/plugin_nodes/strategy/portExpMovingAverageStrategyNode.py
@@ -11,10 +11,10 @@
 def moving_average_signal_kernel(ma_fast, ma_slow, out_arr, arr_len):
     i = cuda.grid(1)
     if i == 0:
-        out_arr[i] = math.inf
+        out_arr[i] = np.nan
     if i < arr_len - 1:
         if math.isnan(ma_slow[i]) or math.isnan(ma_fast[i]):
-            out_arr[i + 1] = math.inf
+            out_arr[i + 1] = np.nan
         elif ma_fast[i] - ma_slow[i] > 0.00001:
             # shift 1 time to make sure no peeking into the future
             out_arr[i + 1] = -1.0
@@ -85,7 +85,8 @@ def process(self, inputs):
         input_df['exp_ma_fast'] = fast
         input_df['exp_ma_fast'] = input_df['exp_ma_fast'].fillna(0.0)
         # remove the bad datapints
-        input_df = input_df.query('signal<10 and indicator == 0')
+        input_df = input_df.dropna()
+        input_df = input_df.query('indicator == 0')
         return input_df
 
 
@@ -128,6 +129,7 @@ def process(self, inputs):
         input_df = input_df.groupby("asset").apply(fun)
         return input_df.dropna(subset=['signal'])
 
+
 if __name__ == "__main__":
     from gquant.dataloader.csvStockLoader import CsvStockLoader
     from gquant.transform.assetFilterNode import AssetFilterNode
diff --git a/gquant/plugin_nodes/strategy/xgboostStrategyNode.py b/gquant/plugin_nodes/strategy/xgboostStrategyNode.py
index e91c196c..b7e7610f 100644
--- a/gquant/plugin_nodes/strategy/xgboostStrategyNode.py
+++ b/gquant/plugin_nodes/strategy/xgboostStrategyNode.py
@@ -4,7 +4,7 @@
 import xgboost as xgb
 from numba import cuda
 import math
-
+import numpy as np
 
 __all__ = ['XGBoostStrategyNode']
 
@@ -13,10 +13,10 @@
 def signal_kernel(signal_arr, out_arr, arr_len):
     i = cuda.grid(1)
     if i == 0:
-        out_arr[i] = math.inf
+        out_arr[i] = np.nan
     if i < arr_len - 1:
         if math.isnan(signal_arr[i]):
-            out_arr[i + 1] = math.inf
+            out_arr[i + 1] = np.nan
         elif signal_arr[i] < 0.0:
             # shift 1 time to make sure no peeking into the future
             out_arr[i + 1] = -1.0
@@ -93,7 +93,6 @@ def process(self, inputs):
                 'scale_pos_weight':  2,
                 'min_child_weight':  30,
                 'tree_method':       'gpu_hist',
-                'n_gpus':            1,
                 'distributed_dask':  True,
                 'loss':              'ls',
                 # 'objective':         'gpu:reg:linear',
@@ -126,7 +125,7 @@ def process(self, inputs):
         signal = compute_signal(prediction)
         input_df['signal'] = signal
         # remove the bad datapints
-        input_df = input_df.query('signal<10')
+        input_df = input_df.dropna()
         remaining = list(self.conf['no_feature'].keys()) + ['signal']
         return input_df[remaining]
 
diff --git a/gquant/plugin_nodes/transform/indicatorNode.py b/gquant/plugin_nodes/transform/indicatorNode.py
index 05a0dadf..0094c8b1 100644
--- a/gquant/plugin_nodes/transform/indicatorNode.py
+++ b/gquant/plugin_nodes/transform/indicatorNode.py
@@ -1,5 +1,4 @@
 from gquant.dataframe_flow import Node
-import numpy as np
 import gquant.cuindicator as ci
 
 
@@ -52,7 +51,6 @@ def process(self, inputs):
         """
         input_df = inputs[0]
         indicators = self.conf['indicators']
-        out_cols = []
         for indicator in indicators:
             fun = getattr(ci, indicator['function'])
             parallel = [input_df['indicator']]
@@ -65,19 +63,15 @@ def process(self, inputs):
                 for out in indicator['outputs']:
                     out_col = self._compose_name(indicator, [out])
                     input_df[out_col] = getattr(v, out)
-                    out_cols.append(out_col)
+                    # out_cols.append(out_col)
             else:
                 if isinstance(v, tuple):
                     v = v[0]
                 out_col = self._compose_name(indicator, [])
                 input_df[out_col] = v
-                out_cols.append(out_col)
         # remove all the na elements, requires cudf>=0.8
         if "remove_na" in self.conf and self.conf["remove_na"]:
-            na_element = input_df[out_cols[0]].isna()
-            for i in range(1, len(out_cols)):
-                na_element |= input_df[out_cols[i]].isna()
-            input_df = input_df.iloc[np.where((~na_element).to_array())[0]]
+            input_df = input_df.dropna()
         return input_df
 
 
diff --git a/gquant/plugin_nodes/transform/returnFeatureNode.py b/gquant/plugin_nodes/transform/returnFeatureNode.py
index 4d274168..44182131 100644
--- a/gquant/plugin_nodes/transform/returnFeatureNode.py
+++ b/gquant/plugin_nodes/transform/returnFeatureNode.py
@@ -72,6 +72,7 @@ def process(self, inputs):
         input_df = input_df.groupby('asset').apply(clean)
         return input_df.dropna()
 
+
 if __name__ == "__main__":
     from gquant.dataloader.csvStockLoader import CsvStockLoader
 
diff --git a/notebooks/01_tutorial.ipynb b/notebooks/01_tutorial.ipynb
index 7b51c497..da3a3b4f 100644
--- a/notebooks/01_tutorial.ipynb
+++ b/notebooks/01_tutorial.ipynb
@@ -202,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -212,13 +212,11 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 3,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "import nxpd\n",
     "from gquant.dataframe_flow import TaskGraph\n",
     "\n",
     "# list of nodes composing the task graph\n",
@@ -229,7 +227,7 @@
     "    task_outputCsv1, task_outputCsv2]\n",
     "\n",
     "task_graph = TaskGraph(task_list)\n",
-    "nxpd.draw(task_graph.viz_graph(), show='ipynb')"
+    "task_graph.draw(show='ipynb')"
    ]
   },
   {
@@ -243,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -304,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -314,14 +312,13 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 6,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
     "task_graph = TaskGraph.load_taskgraph(task_graph_file_name)\n",
-    "nxpd.draw(task_graph.viz_graph(), show='ipynb')"
+    "task_graph.draw(show='ipynb')"
    ]
   },
   {
diff --git a/notebooks/02_single_stock_trade.ipynb b/notebooks/02_single_stock_trade.ipynb
index 1ad14ed6..6284b316 100644
--- a/notebooks/02_single_stock_trade.ipynb
+++ b/notebooks/02_single_stock_trade.ipynb
@@ -19,7 +19,6 @@
     "import os\n",
     "import warnings\n",
     "import ipywidgets as widgets\n",
-    "import nxpd\n",
     "from gquant.dataframe_flow import TaskGraph\n",
     "\n",
     "warnings.simplefilter(\"ignore\")"
@@ -98,14 +97,13 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 3,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
     "task_graph = TaskGraph.load_taskgraph('../task_example/simple_trade.yaml')\n",
-    "nxpd.draw(task_graph.viz_graph(), show='ipynb')"
+    "task_graph.draw(show='ipynb')"
    ]
   },
   {
@@ -182,7 +180,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "966d53dfcc4c413b93fa680c7ebfefaf",
+       "model_id": "00b25c642fa74c5eab902ceab69aaa0b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -223,7 +221,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "13a1785a2db6451da771d4cf3dfd3997",
+       "model_id": "fb69a20850a64b47959ef874fb53da85",
        "version_major": 2,
        "version_minor": 0
       },
@@ -255,7 +253,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9f3f8e8085224746b777d80ea435b9ae",
+       "model_id": "06455685c2c24ecd9f03f57f89a0f286",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/notebooks/03_simple_dask_example.ipynb b/notebooks/03_simple_dask_example.ipynb
index e21b36dd..c3076c1e 100644
--- a/notebooks/03_simple_dask_example.ipynb
+++ b/notebooks/03_simple_dask_example.ipynb
@@ -9,9 +9,7 @@
     "import sys\n",
     "sys.path.append('..')\n",
     "\n",
-    "from gquant.dataframe_flow import TaskGraph\n",
-    "import nxpd\n",
-    "from nxpd import draw"
+    "from gquant.dataframe_flow import TaskGraph"
    ]
   },
   {
@@ -27,23 +25,23 @@
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3 style=\"text-align: left;\">Client</h3>\n",
        "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
-       "  <li><b>Scheduler: </b>tcp://127.0.0.1:40106</li>\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:46190</li>\n",
        "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
        "</ul>\n",
        "</td>\n",
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3 style=\"text-align: left;\">Cluster</h3>\n",
        "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
-       "  <li><b>Workers: </b>4</li>\n",
-       "  <li><b>Cores: </b>4</li>\n",
-       "  <li><b>Memory: </b>270.38 GB</li>\n",
+       "  <li><b>Workers: </b>8</li>\n",
+       "  <li><b>Cores: </b>8</li>\n",
+       "  <li><b>Memory: </b>540.95 GB</li>\n",
        "</ul>\n",
        "</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "text/plain": [
-       "<Client: scheduler='tcp://127.0.0.1:40106' processes=4 cores=4>"
+       "<Client: 'tcp://127.0.0.1:46190' processes=8 threads=8, memory=540.95 GB>"
       ]
      },
      "execution_count": 2,
@@ -266,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -276,14 +274,13 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
     "task_graph = TaskGraph.load_taskgraph('../task_example/dask_task.yaml')\n",
-    "draw(task_graph.viz_graph(), show='ipynb')"
+    "task_graph.draw(show='ipynb')"
    ]
   },
   {
diff --git a/notebooks/04_portfolio_trade.ipynb b/notebooks/04_portfolio_trade.ipynb
index a3883ba7..c0327bf2 100644
--- a/notebooks/04_portfolio_trade.ipynb
+++ b/notebooks/04_portfolio_trade.ipynb
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -143,18 +143,16 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 3,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
     "import sys ; sys.path.append('..')\n",
-    "import nxpd\n",
     "from gquant.dataframe_flow import TaskGraph\n",
     "\n",
     "task_graph = TaskGraph.load_taskgraph('../task_example/port_trade.yaml')\n",
-    "nxpd.draw(task_graph.viz_graph(), show='ipynb')"
+    "task_graph.draw(show='ipynb')"
    ]
   },
   {
@@ -267,8 +265,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 5.51 s, sys: 2.98 s, total: 8.49 s\n",
-      "Wall time: 12.9 s\n"
+      "CPU times: user 4.89 s, sys: 3.11 s, total: 8 s\n",
+      "Wall time: 14.7 s\n"
      ]
     }
    ],
@@ -333,12 +331,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0e6c9d5765574525bc9e0e8395fe61eb",
+       "model_id": "9988d64d19df4bfb820df823d42a5a43",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale(), side='left'), Axis(l…"
+       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale()), Axis(label='Time', …"
       ]
      },
      "metadata": {},
@@ -384,7 +382,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 2min 10s, sys: 14.6 s, total: 2min 25s\n",
+      "CPU times: user 2min 9s, sys: 14.8 s, total: 2min 24s\n",
       "Wall time: 2min 24s\n"
      ]
     }
@@ -413,12 +411,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2d9a61f04c7442cca77867fe2455c96d",
+       "model_id": "ac66026af35f450fac2c28b92a3f10eb",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale(), side='left'), Axis(l…"
+       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale()), Axis(label='Time', …"
       ]
      },
      "metadata": {},
@@ -473,7 +471,7 @@
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3 style=\"text-align: left;\">Client</h3>\n",
        "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
-       "  <li><b>Scheduler: </b>tcp://127.0.0.1:43265</li>\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:35971</li>\n",
        "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
        "</ul>\n",
        "</td>\n",
@@ -489,7 +487,7 @@
        "</table>"
       ],
       "text/plain": [
-       "<Client: scheduler='tcp://127.0.0.1:43265' processes=8 cores=8>"
+       "<Client: 'tcp://127.0.0.1:35971' processes=8 threads=8, memory=540.95 GB>"
       ]
      },
      "execution_count": 11,
@@ -556,15 +554,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 32.1 s, sys: 6.21 s, total: 38.3 s\n",
-      "Wall time: 2min 25s\n"
+      "CPU times: user 21.3 s, sys: 4.3 s, total: 25.6 s\n",
+      "Wall time: 1min 25s\n"
      ]
     }
    ],
@@ -584,18 +582,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4b62bf46582249dba47ef1a10af2c944",
+       "model_id": "5fc144523eb6412c97369c56546693dd",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale()), Axis(label='Time', …"
+       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale(), side='left'), Axis(l…"
       ]
      },
      "metadata": {},
@@ -633,13 +631,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a37cc24f0c674c7790962109068cc0d0",
+       "model_id": "f0c77510aca34d9ba4ad21a108585e1e",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/notebooks/05_customize_nodes.ipynb b/notebooks/05_customize_nodes.ipynb
index c621fa0e..288057eb 100644
--- a/notebooks/05_customize_nodes.ipynb
+++ b/notebooks/05_customize_nodes.ipynb
@@ -24,10 +24,8 @@
     "# Load necessary Python modules\n",
     "import sys\n",
     "from gquant.dataframe_flow import TaskGraph, Node\n",
-    "import nxpd\n",
     "import cudf\n",
     "import numpy as np\n",
-    "from nxpd import draw\n",
     "from numba import cuda\n",
     "import cupy\n",
     "import math\n",
@@ -130,9 +128,8 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -150,7 +147,7 @@
     "\n",
     "task_list = [input_node, cudf_distance_node]\n",
     "task_graph = TaskGraph(task_list)\n",
-    "draw(task_graph.viz_graph(), show='ipynb')"
+    "task_graph.draw(show='ipynb')"
    ]
   },
   {
@@ -170,67 +167,67 @@
      "output_type": "stream",
      "text": [
       "            x         y  distance_cudf\n",
-      "0    0.494728  0.794826       0.936218\n",
-      "1    0.039471  0.887278       0.888155\n",
-      "2    0.895870  0.955368       1.309698\n",
-      "3    0.244685  0.542005       0.594677\n",
-      "4    0.926441  0.890042       1.284705\n",
-      "5    0.244099  0.647376       0.691867\n",
-      "6    0.174271  0.094161       0.198083\n",
-      "7    0.105868  0.026643       0.109169\n",
-      "8    0.310617  0.391588       0.499824\n",
-      "9    0.022745  0.709048       0.709412\n",
-      "10   0.820890  0.734814       1.101732\n",
-      "11   0.652485  0.169152       0.674054\n",
-      "12   0.913051  0.192264       0.933074\n",
-      "13   0.551375  0.025944       0.551985\n",
-      "14   0.468520  0.915554       1.028469\n",
-      "15   0.470745  0.929401       1.041819\n",
-      "16   0.817355  0.259037       0.857420\n",
-      "17   0.734461  0.498428       0.887617\n",
-      "18   0.596999  0.667664       0.895646\n",
-      "19   0.635199  0.396167       0.748617\n",
-      "20   0.295510  0.830177       0.881204\n",
-      "21   0.861023  0.538919       1.015773\n",
-      "22   0.615989  0.140955       0.631910\n",
-      "23   0.813611  0.774649       1.123407\n",
-      "24   0.061387  0.210559       0.219326\n",
-      "25   0.416541  0.631203       0.756256\n",
-      "26   0.092946  0.186763       0.208613\n",
-      "27   0.276204  0.255798       0.376458\n",
-      "28   0.426326  0.396049       0.581901\n",
-      "29   0.875708  0.287663       0.921746\n",
+      "0    0.836387  0.957514       1.271368\n",
+      "1    0.294106  0.109366       0.313782\n",
+      "2    0.495487  0.440543       0.663013\n",
+      "3    0.998370  0.553888       1.141724\n",
+      "4    0.691739  0.253014       0.736559\n",
+      "5    0.000852  0.061719       0.061725\n",
+      "6    0.765695  0.470009       0.898441\n",
+      "7    0.170214  0.214503       0.273833\n",
+      "8    0.567862  0.188615       0.598367\n",
+      "9    0.128882  0.536904       0.552157\n",
+      "10   0.752010  0.497383       0.901615\n",
+      "11   0.027600  0.129488       0.132397\n",
+      "12   0.714133  0.242321       0.754126\n",
+      "13   0.051390  0.874764       0.876272\n",
+      "14   0.424406  0.113438       0.439304\n",
+      "15   0.587216  0.961709       1.126812\n",
+      "16   0.478085  0.133467       0.496366\n",
+      "17   0.363664  0.157096       0.396145\n",
+      "18   0.624918  0.109936       0.634514\n",
+      "19   0.581437  0.400406       0.705970\n",
+      "20   0.961781  0.454890       1.063931\n",
+      "21   0.907983  0.740077       1.171387\n",
+      "22   0.408558  0.849442       0.942587\n",
+      "23   0.949032  0.475539       1.061508\n",
+      "24   0.212582  0.999529       1.021885\n",
+      "25   0.838425  0.166519       0.854801\n",
+      "26   0.564560  0.166126       0.588494\n",
+      "27   0.768162  0.282583       0.818490\n",
+      "28   0.758121  0.827895       1.122567\n",
+      "29   0.925646  0.353130       0.990718\n",
       "..        ...       ...            ...\n",
-      "970  0.539878  0.956934       1.098723\n",
-      "971  0.847171  0.375360       0.926603\n",
-      "972  0.875900  0.419927       0.971359\n",
-      "973  0.179465  0.639737       0.664433\n",
-      "974  0.185767  0.941486       0.959638\n",
-      "975  0.445594  0.444433       0.629344\n",
-      "976  0.941624  0.722191       1.186683\n",
-      "977  0.622220  0.972430       1.154460\n",
-      "978  0.410024  0.073384       0.416539\n",
-      "979  0.982785  0.485625       1.096220\n",
-      "980  0.966963  0.632969       1.155711\n",
-      "981  0.424788  0.557915       0.701223\n",
-      "982  0.158918  0.641006       0.660411\n",
-      "983  0.205247  0.292902       0.357656\n",
-      "984  0.620553  0.427306       0.753443\n",
-      "985  0.803859  0.023703       0.804208\n",
-      "986  0.680478  0.765732       1.024400\n",
-      "987  0.701616  0.537132       0.883616\n",
-      "988  0.936694  0.372176       1.007924\n",
-      "989  0.721997  0.648893       0.970743\n",
-      "990  0.143095  0.886502       0.897977\n",
-      "991  0.886398  0.250655       0.921157\n",
-      "992  0.864329  0.947291       1.282351\n",
-      "993  0.869851  0.932506       1.275229\n",
-      "994  0.145058  0.502570       0.523085\n",
-      "995  0.277142  0.911574       0.952772\n",
-      "996  0.858415  0.729754       1.126684\n",
-      "997  0.439982  0.937030       1.035185\n",
-      "998  0.801104  0.308690       0.858521\n",
-      "999  0.485041  0.024730       0.485671\n",
+      "970  0.125576  0.018615       0.126948\n",
+      "971  0.826058  0.344783       0.895124\n",
+      "972  0.759709  0.774125       1.084632\n",
+      "973  0.579127  0.659230       0.877481\n",
+      "974  0.446206  0.001899       0.446210\n",
+      "975  0.470431  0.918708       1.032148\n",
+      "976  0.615124  0.183615       0.641944\n",
+      "977  0.925549  0.488118       1.046375\n",
+      "978  0.578353  0.889417       1.060922\n",
+      "979  0.216710  0.790887       0.820040\n",
+      "980  0.219122  0.240300       0.325206\n",
+      "981  0.539036  0.036415       0.540265\n",
+      "982  0.111227  0.477055       0.489850\n",
+      "983  0.927727  0.835949       1.248795\n",
+      "984  0.336382  0.859899       0.923352\n",
+      "985  0.969154  0.858187       1.294505\n",
+      "986  0.281598  0.562810       0.629327\n",
+      "987  0.934636  0.939830       1.325453\n",
+      "988  0.438150  0.028522       0.439077\n",
+      "989  0.549242  0.387627       0.672251\n",
+      "990  0.543592  0.493093       0.733916\n",
+      "991  0.599601  0.560348       0.820678\n",
+      "992  0.683408  0.402713       0.793236\n",
+      "993  0.366721  0.283478       0.463513\n",
+      "994  0.054395  0.082871       0.099128\n",
+      "995  0.345603  0.094344       0.358248\n",
+      "996  0.902845  0.954603       1.313924\n",
+      "997  0.411790  0.047897       0.414566\n",
+      "998  0.507562  0.226668       0.555875\n",
+      "999  0.575396  0.145080       0.593404\n",
       "\n",
       "[1000 rows x 3 columns]\n"
      ]
@@ -284,9 +281,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from librmm_cffi import librmm as rmm\n",
-    "\n",
-    "\n",
     "class NumbaDistanceNode(Node):\n",
     "\n",
     "    def columns_setup(self,):\n",
@@ -413,9 +407,8 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 11,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -437,7 +430,7 @@
     "             cupy_distance_node, cudf_distance_node]\n",
     "out_list = ['distance_by_numba', 'distance_by_cupy', 'distance_by_cudf']\n",
     "task_graph = TaskGraph(task_list)\n",
-    "draw(task_graph.viz_graph(), show='ipynb')"
+    "task_graph.draw(show='ipynb')"
    ]
   },
   {
@@ -508,23 +501,23 @@
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3 style=\"text-align: left;\">Client</h3>\n",
        "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
-       "  <li><b>Scheduler: </b>tcp://127.0.0.1:36835</li>\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:45147</li>\n",
        "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
        "</ul>\n",
        "</td>\n",
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3 style=\"text-align: left;\">Cluster</h3>\n",
        "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
-       "  <li><b>Workers: </b>4</li>\n",
-       "  <li><b>Cores: </b>4</li>\n",
-       "  <li><b>Memory: </b>270.38 GB</li>\n",
+       "  <li><b>Workers: </b>8</li>\n",
+       "  <li><b>Cores: </b>8</li>\n",
+       "  <li><b>Memory: </b>540.95 GB</li>\n",
        "</ul>\n",
        "</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "text/plain": [
-       "<Client: scheduler='tcp://127.0.0.1:36835' processes=4 cores=4>"
+       "<Client: 'tcp://127.0.0.1:45147' processes=8 threads=8, memory=540.95 GB>"
       ]
      },
      "execution_count": 14,
@@ -635,9 +628,8 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 17,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -682,7 +674,7 @@
     "             numba_distance_node, cupy_distance_node]\n",
     "out_list = ['distance_by_numba', 'distance_by_cupy', 'distance_by_cudf']\n",
     "task_graph = TaskGraph(task_list)\n",
-    "draw(task_graph.viz_graph(), show='ipynb')"
+    "task_graph.draw(show='ipynb')"
    ]
   },
   {
@@ -814,7 +806,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Writing custom_nodes.py\n"
+      "Overwriting custom_nodes.py\n"
      ]
     }
    ],
@@ -947,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -957,9 +949,8 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 22,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1009,12 +1000,12 @@
     "             numba_distance_node, cupy_distance_node]\n",
     "out_list = ['distance_by_numba', 'distance_by_cupy', 'distance_by_cudf']\n",
     "task_graph = TaskGraph(task_list)\n",
-    "draw(task_graph.viz_graph(), show='ipynb')"
+    "task_graph.draw(show='ipynb')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1063,7 +1054,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/notebooks/06_xgboost_trade.ipynb b/notebooks/06_xgboost_trade.ipynb
index 864d2da0..903f026b 100644
--- a/notebooks/06_xgboost_trade.ipynb
+++ b/notebooks/06_xgboost_trade.ipynb
@@ -28,9 +28,7 @@
     "\n",
     "import warnings\n",
     "from gquant.dataframe_flow import TaskGraph\n",
-    "import nxpd\n",
     "import ipywidgets as widgets\n",
-    "from nxpd import draw\n",
     "import os\n",
     "\n",
     "warnings.simplefilter(\"ignore\")"
@@ -70,7 +68,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.9.0\n"
+      "0.10.0\n"
      ]
     }
    ],
@@ -159,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -169,14 +167,13 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
     "task_graph = TaskGraph.load_taskgraph('../task_example/xgboost_trade.yaml')\n",
-    "draw(task_graph.viz_graph(), show='ipynb')"
+    "task_graph.draw(show='ipynb')"
    ]
   },
   {
@@ -250,7 +247,6 @@
       "                'scale_pos_weight':  2,\n",
       "                'min_child_weight':  30,\n",
       "                'tree_method':       'gpu_hist',\n",
-      "                'n_gpus':            1,\n",
       "                'distributed_dask':  True,\n",
       "                'loss':              'ls',\n",
       "                # 'objective':         'gpu:reg:linear',\n",
@@ -283,7 +279,7 @@
       "        signal = compute_signal(prediction)\n",
       "        input_df['signal'] = signal\n",
       "        # remove the bad datapints\n",
-      "        input_df = input_df.query('signal<10')\n",
+      "        input_df = input_df.dropna()\n",
       "        remaining = list(self.conf['no_feature'].keys()) + ['signal']\n",
       "        return input_df[remaining]\n",
       "\n"
@@ -383,7 +379,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a3d2fd447f4d4417b13d79ceef111011",
+       "model_id": "3ea35da1b6684e4baa8f23b48daf356a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -558,9 +554,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9587710d3852467bac5932d35a4d4542",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale()), Axis…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "replace_spec['node_technical_indicator'] = {\"conf\": indicator_conf}\n",
     "replace_spec['node_sort2'] = {\"load\": cached_sort}\n",
@@ -595,7 +606,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e95d3670cb0340879355f6ef0e35f61e",
+       "model_id": "5cd0350b70f6426c935e3313c688a554",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/notebooks/07_fractional_differencing.ipynb b/notebooks/07_fractional_differencing.ipynb
index f063ac00..b62e55e5 100644
--- a/notebooks/07_fractional_differencing.ipynb
+++ b/notebooks/07_fractional_differencing.ipynb
@@ -27,15 +27,15 @@
     "import warnings\n",
     "import gquant\n",
     "from gquant.cuindicator import get_weights_floored, fractional_diff\n",
-    "import nxpd\n",
     "import ipywidgets as widgets\n",
-    "from nxpd import draw\n",
     "import os\n",
     "import time\n",
     "import numpy as np\n",
     "from numba import cuda\n",
     "import cudf\n",
     "import inspect\n",
+    "from numba import njit\n",
+    "from numba import prange\n",
     "warnings.simplefilter(\"ignore\")"
    ]
   },
@@ -354,24 +354,72 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Fractional differencing is essentially doing 1D convolution computation with the kernel values set to be the weights computed from `get_weights_floored`. Check the original [notebook](https://github.com/ritchieng/fractional_differencing_gpu/blob/master/notebooks/gpu_fractional_differencing.ipynb) for the details of the meanings of the weights. To make convolution computation faster, we divide the long input array into small chunks and send to different thread blocks. All the array chunks and the weights are loaded into the GPU shared memory for fast IO. The device function `conv_window` is doing the convolution computation for one thread.\n",
+    "Fractional differencing is essentially doing 1D convolution computation with the kernel values set to be the weights computed from get_weights_floored. Check the original notebook for the details of the meanings of the weights. To make convolution computation faster, we divide the long input array into small chunks and send to different thread blocks. All the array chunks and the weights are loaded into the GPU shared memory for fast IO. The device function conv_window is doing the convolution computation for one thread.\n",
     "\n",
-    "We can compare the performance of gQuant implementation vs the original one:"
+    "To make a fair comparsion with CPU implementation, we implemented an efficient CPU version of the fractional differencing calculation. It is accelerated by numba.njit that take advantage of multiple cores of the CPU and fastmath compiler optimization."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "@njit(fastmath=True, parallel=True)\n",
+    "def moving_dot_product_cpu(in_data, out, window_size, weights):\n",
+    "    # Set the first window_size-1 rows in each chunk to np.nan due \n",
+    "    # insufficient history\n",
+    "    for i in prange(0, window_size - 1):\n",
+    "        out[i] = np.nan\n",
+    "    \n",
+    "    # Compute dot product of preceding window_size rows\n",
+    "    for i in prange(window_size - 1, len(in_data)):\n",
+    "        rolling_dot_product = 0.0\n",
+    "        \n",
+    "        k = 0\n",
+    "        for j in range(i - window_size + 1, i + 1):\n",
+    "            rolling_dot_product += in_data[j] * weights[k]\n",
+    "            k += 1\n",
+    "        \n",
+    "        out[i] = rolling_dot_product \n",
+    "\n",
+    "def cpu_fractional_diff(input_arr, d=0.5, floor=1e-3):\n",
+    "\n",
+    "    # compute the weights for the fractional difference\n",
+    "    weights = get_weights_floored(d=d,\n",
+    "                                  num_k=len(input_arr),\n",
+    "                                  floor=floor)[::-1, 0]\n",
+    "    weights_out = np.ascontiguousarray(weights)\n",
+    "    weights = weights_out\n",
+    "    weights_window_size = len(weights)\n",
+    "    window = len(weights)\n",
+    "    out = np.zeros_like(input_arr)\n",
+    "    moving_dot_product_cpu(input_arr, out, weights_window_size, weights)\n",
+    "    return out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fractional differencing is essentially doing 1D convolution computation with the kernel values set to be the weights computed from `get_weights_floored`. Check the original [notebook](https://github.com/ritchieng/fractional_differencing_gpu/blob/master/notebooks/gpu_fractional_differencing.ipynb) for the details of the meanings of the weights. To make convolution computation faster, we divide the long input array into small chunks and send to different thread blocks. All the array chunks and the weights are loaded into the GPU shared memory for fast IO. The device function `conv_window` is doing the convolution computation for one thread.\n",
+    "\n",
+    "We can compare the performance of gQuant GPU implementation vs the original one and CPU implementation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "array size 100000, Ensemble: time 0.507 s, gQuant Time 0.408 s, speed up 1.24, error 0.0000 \n",
-      "array size 1000000, Ensemble: time 0.079 s, gQuant Time 0.004 s, speed up 22.30, error 0.0000 \n",
-      "array size 10000000, Ensemble: time 0.669 s, gQuant Time 0.008 s, speed up 87.18, error 0.0000 \n",
-      "array size 100000000, Ensemble: time 6.455 s, gQuant Time 0.052 s, speed up 124.65, error 0.0000 \n"
+      "array size 100000, Ensemble: time 0.436 s, gQuant GPU Time 0.397 s, gQuant CPU Time 0.666, speed up 1.10, speed up vs CPU 1.68, error 0.0000 \n",
+      "array size 1000000, Ensemble: time 0.076 s, gQuant GPU Time 0.004 s, gQuant CPU Time 0.031, speed up 19.45, speed up vs CPU 7.91, error 0.0000 \n",
+      "array size 10000000, Ensemble: time 0.628 s, gQuant GPU Time 0.007 s, gQuant CPU Time 0.169, speed up 85.16, speed up vs CPU 22.85, error 0.0000 \n",
+      "array size 100000000, Ensemble: time 5.854 s, gQuant GPU Time 0.049 s, gQuant CPU Time 1.672, speed up 120.59, speed up vs CPU 34.45, error 0.0000 \n"
      ]
     }
    ],
@@ -394,17 +442,25 @@
     "    gquant_gpu, weights = fractional_diff(df_raw2['in'], d=0.5, floor=5e-5)\n",
     "    cuda.synchronize()\n",
     "    end = time.time()\n",
+    "    optimized_duration = end - start\n",
     "    #(df_raw_fd_from_gpu.values)\n",
     "    \n",
-    "    err = np.abs(df_raw_fd_from_gpu['out'].to_array() - np.array(gquant_gpu)[weights.size-1:]).max()\n",
-    "    print('array size %d, Ensemble: time %.3f s, gQuant Time %.3f s, speed up %.2f, error %.4f ' % (10**int(i), duration, end - start, duration / (end-start), err))\n"
+    "    \n",
+    "    start = time.time()\n",
+    "    cpu_result = cpu_fractional_diff(ran_array, d=0.5, floor=5e-5)\n",
+    "    end = time.time()\n",
+    "    cpu_duration = end - start\n",
+    "    \n",
+    "    err = np.abs(df_raw_fd_from_gpu['out'].to_array()[weights.size-1:] - np.array(gquant_gpu)[weights.size-1:]).max()\n",
+    "    err = max(np.abs(df_raw_fd_from_gpu['out'].to_array()[weights.size-1:] - cpu_result[weights.size-1:]).max(), err)\n",
+    "    print('array size %d, Ensemble: time %.3f s, gQuant GPU Time %.3f s, gQuant CPU Time %.3f, speed up %.2f, speed up vs CPU %.2f, error %.4f ' % (10**int(i), duration, optimized_duration, cpu_duration, duration / optimized_duration, cpu_duration/optimized_duration, err))\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For the array of length 100m, gQuant can achieve 100x speedup compare with the Ensemble Capitial's GPU implementatoin. "
+    "For the array of length 100m, gQuant can achieve 100x speedup compare with the Ensemble Capitial's GPU implementatoin and 30x speed up compared with multiple core CPU."
    ]
   },
   {
@@ -443,7 +499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -453,9 +509,8 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 6,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -466,13 +521,11 @@
     "import gquant\n",
     "from gquant.dataframe_flow import TaskGraph\n",
     "import ipywidgets as widgets\n",
-    "import nxpd\n",
-    "from nxpd import draw\n",
     "import warnings\n",
     "warnings.simplefilter(\"ignore\")\n",
     "\n",
     "task_graph = TaskGraph.load_taskgraph('../task_example/xgboost_trade.yaml')\n",
-    "draw(task_graph.viz_graph(), show='ipynb')"
+    "task_graph.draw(show='ipynb')"
    ]
   },
   {
@@ -484,7 +537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -502,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -534,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -578,13 +631,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f7d7488ae3104d409d51069157438980",
+       "model_id": "63310421c9df4bea9037e1f80e600e3b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -627,7 +680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -637,9 +690,8 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 11,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -672,7 +724,7 @@
     "     \"title\": \"Signals\"},\n",
     "   \"inputs\": [\"node_filter_asset\"]}\n",
     "task_graph.extend([asset_filter, node_lines])\n",
-    "draw(task_graph.viz_graph(), show='ipynb')"
+    "task_graph.draw(show='ipynb')"
    ]
   },
   {
@@ -684,13 +736,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cefa0bc87bdb4efe9c21807b73aa60a0",
+       "model_id": "1c95626aa436451ebe4939cc7fd1ffce",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/notebooks/mortgage_e2e_gquant/mortgage_e2e_gquant.ipynb b/notebooks/mortgage_e2e_gquant/mortgage_e2e_gquant.ipynb
index 2f9a0094..ca6bf590 100644
--- a/notebooks/mortgage_e2e_gquant/mortgage_e2e_gquant.ipynb
+++ b/notebooks/mortgage_e2e_gquant/mortgage_e2e_gquant.ipynb
@@ -290,11 +290,10 @@
    ],
    "source": [
     "from gquant.dataframe_flow import TaskGraph\n",
-    "from nxpd import draw\n",
     "\n",
     "task_spec_list = mortgage_etl_workflow_def()\n",
     "task_graph = TaskGraph(task_spec_list)\n",
-    "draw(task_graph.viz_graph(), show='ipynb')"
+    "task_graph.draw(show='ipynb')"
    ]
   },
   {
@@ -508,9 +507,6 @@
    "source": [
     "import os\n",
     "import json\n",
-    "\n",
-    "from nxpd import draw\n",
-    "\n",
     "from gquant.dataframe_flow import (TaskSpecSchema, TaskGraph)\n",
     "\n",
     "from mortgage_common import (\n",
@@ -602,7 +598,7 @@
     "\n",
     "task_spec_list = [mortgage_workflow_runner_task, xgb_trainer_task]\n",
     "task_graph = TaskGraph(task_spec_list)\n",
-    "draw(task_graph.viz_graph(), show='ipynb')"
+    "task_graph.draw(show='ipynb')"
    ]
   },
   {
@@ -981,10 +977,7 @@
    ],
    "source": [
     "import os\n",
-    "from nxpd import draw\n",
-    "\n",
     "from gquant.dataframe_flow import (TaskSpecSchema, TaskGraph)\n",
-    "\n",
     "from mortgage_common import (\n",
     "    mortgage_etl_workflow_def, generate_mortgage_gquant_run_params_list,\n",
     "    MortgageTaskNames)\n",
@@ -1095,7 +1088,7 @@
     "task_spec_list = [mortgage_workflow_runner_task, dxgb_trainer_task]\n",
     "\n",
     "task_graph = TaskGraph(task_spec_list)\n",
-    "draw(task_graph.viz_graph(), show='ipynb')"
+    "task_graph.draw(show='ipynb')"
    ]
   },
   {
@@ -1272,7 +1265,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,
diff --git a/tests/unit/test_fractional_diff.py b/tests/unit/test_fractional_diff.py
index 0d0ccf95..dc7a5cb3 100644
--- a/tests/unit/test_fractional_diff.py
+++ b/tests/unit/test_fractional_diff.py
@@ -74,7 +74,7 @@ def setUp(self):
         warnings.filterwarnings('ignore', message='numpy.ufunc size changed')
         array_len = int(1e4)
         random_array = np.random.rand(array_len)
-        df = cudf.dataframe.DataFrame()
+        df = cudf.DataFrame()
         df['in'] = random_array
 
         pdf = pd.DataFrame()
@@ -94,7 +94,7 @@ def setUp(self):
         indicator = np.zeros(size, dtype=np.int32)
         indicator[0] = 1
         indicator[half] = 1
-        df2 = cudf.dataframe.DataFrame()
+        df2 = cudf.DataFrame()
         df2['in'] = random_array
         df2['indicator'] = indicator
 
diff --git a/tests/unit/test_indicator_node.py b/tests/unit/test_indicator_node.py
index 42f29657..f71db06c 100644
--- a/tests/unit/test_indicator_node.py
+++ b/tests/unit/test_indicator_node.py
@@ -53,7 +53,7 @@ def setUp(self):
         indicator = np.zeros(size, dtype=np.int32)
         indicator[0] = 1
         indicator[half] = 1
-        df = cudf.dataframe.DataFrame()
+        df = cudf.DataFrame()
         df['in'] = random_array
         df['open'] = open_array
         df['close'] = close_array
diff --git a/tests/unit/test_multi_assets_indicator.py b/tests/unit/test_multi_assets_indicator.py
index 550e4eed..d3fa6d66 100644
--- a/tests/unit/test_multi_assets_indicator.py
+++ b/tests/unit/test_multi_assets_indicator.py
@@ -53,7 +53,7 @@ def setUp(self):
         indicator = np.zeros(size, dtype=np.int32)
         indicator[0] = 1
         indicator[half] = 1
-        df = cudf.dataframe.DataFrame()
+        df = cudf.DataFrame()
         df['in'] = random_array
         df['open'] = open_array
         df['close'] = close_array
diff --git a/tests/unit/test_rolling.py b/tests/unit/test_rolling.py
index 50d03422..1451af2f 100644
--- a/tests/unit/test_rolling.py
+++ b/tests/unit/test_rolling.py
@@ -37,7 +37,7 @@ def setUp(self):
         self.average_window = 300
         random_array = np.random.rand(array_len)
 
-        df = cudf.dataframe.DataFrame()
+        df = cudf.DataFrame()
         df['in'] = random_array
 
         pdf = pd.DataFrame()
diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index 7f24b7e1..5e06c36f 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -37,7 +37,7 @@ def setUp(self):
         self.average_window = 300
         random_array = np.random.rand(array_len)
 
-        df = cudf.dataframe.DataFrame()
+        df = cudf.DataFrame()
         df['in'] = random_array
 
         pdf = pd.DataFrame()

From c501a7595e80a89d5eedbfdf4686f104668b474d Mon Sep 17 00:00:00 2001
From: Alex Volkov <avolkov@nvidia.com>
Date: Thu, 24 Oct 2019 12:12:36 -0700
Subject: [PATCH 2/6] CUQ-36 Fix type check mechanism to handle multi-input
 dataframes.

* add ports API to dataflow tasks.
* ports validation.
* ports compatibility with non-port API.
* add customized nodes notebook demonstratint ports.
* refactor Node class with mixins (ISP).
* add default caching logic for ports API.
* add unit tests for ports Node API and TaskGraph.
---
 gquant/_common.py                             |   15 +
 gquant/dataframe_flow/__init__.py             |    1 +
 gquant/dataframe_flow/_node.py                |   13 +
 gquant/dataframe_flow/_node_flow.py           |  722 ++++++++
 gquant/dataframe_flow/node.py                 |  546 +++---
 gquant/dataframe_flow/portsSpecSchema.py      |  128 ++
 gquant/dataframe_flow/task.py                 |   44 +-
 gquant/dataframe_flow/taskGraph.py            |  236 ++-
 gquant/dataframe_flow/taskSpecSchema.py       |   15 +-
 .../05b_customize_nodes_with_ports.ipynb      | 1586 +++++++++++++++++
 notebooks/custom_port_nodes.py                |  301 ++++
 tests/unit/custom_port_nodes.py               |  109 ++
 tests/unit/test_node_api.py                   |  135 ++
 tests/unit/test_taskgraph_api.py              |  281 +++
 14 files changed, 3797 insertions(+), 335 deletions(-)
 create mode 100644 gquant/_common.py
 create mode 100644 gquant/dataframe_flow/_node.py
 create mode 100644 gquant/dataframe_flow/_node_flow.py
 create mode 100644 gquant/dataframe_flow/portsSpecSchema.py
 create mode 100644 notebooks/05b_customize_nodes_with_ports.ipynb
 create mode 100644 notebooks/custom_port_nodes.py
 create mode 100644 tests/unit/custom_port_nodes.py
 create mode 100644 tests/unit/test_node_api.py
 create mode 100644 tests/unit/test_taskgraph_api.py

diff --git a/gquant/_common.py b/gquant/_common.py
new file mode 100644
index 00000000..0e7d70c7
--- /dev/null
+++ b/gquant/_common.py
@@ -0,0 +1,15 @@
+from collections import namedtuple, Mapping
+
+__all__ = ['_namedtuple_with_defaults']
+
+
+def _namedtuple_with_defaults(typename, field_names, default_values=()):
+    # https://stackoverflow.com/a/18348004/3457624
+    T = namedtuple(typename, field_names)
+    T.__new__.__defaults__ = (None,) * len(T._fields)
+    if isinstance(default_values, Mapping):
+        prototype = T(**default_values)
+    else:
+        prototype = T(*default_values)
+    T.__new__.__defaults__ = tuple(prototype)
+    return T
diff --git a/gquant/dataframe_flow/__init__.py b/gquant/dataframe_flow/__init__.py
index 5986b923..473fc62f 100644
--- a/gquant/dataframe_flow/__init__.py
+++ b/gquant/dataframe_flow/__init__.py
@@ -1,3 +1,4 @@
 from .node import *  # noqa: F401,F403
 from .taskSpecSchema import *  # noqa: F401,F403
 from .taskGraph import *  # noqa: F401,F403
+from .portsSpecSchema import *  # noqa: F401,F403
diff --git a/gquant/dataframe_flow/_node.py b/gquant/dataframe_flow/_node.py
new file mode 100644
index 00000000..5b20873d
--- /dev/null
+++ b/gquant/dataframe_flow/_node.py
@@ -0,0 +1,13 @@
+import abc
+
+
+__all__ = ['_Node']
+
+
+# compatible with Python 2 *and* 3:
+_ABC = abc.ABCMeta('ABC', (object,), {'__slots__': ()})
+
+
+class _Node(_ABC):
+    '''Intermediate class to identify Node class instances and avoid cyclic
+    imports.'''
diff --git a/gquant/dataframe_flow/_node_flow.py b/gquant/dataframe_flow/_node_flow.py
new file mode 100644
index 00000000..310cd245
--- /dev/null
+++ b/gquant/dataframe_flow/_node_flow.py
@@ -0,0 +1,722 @@
+import warnings
+import numpy as np
+import pandas as pd
+import dask
+import cudf
+import dask_cudf
+
+from .taskSpecSchema import TaskSpecSchema
+from .portsSpecSchema import PortsSpecSchema
+
+OUTPUT_ID = 'f291b900-bd19-11e9-aca3-a81e84f29b0f_uni_output'
+
+
+__all__ = ['NodeTaskGraphMixin', 'OUTPUT_ID']
+
+# class NodeIncomingEdge(object):
+#     from_node = 'from_node'
+#     from_port = 'from_port'
+#     to_node = 'to_port'
+#
+#
+# class NodeOutgoingEdge(object):
+#     to_node = 'to_node'
+#     to_port = 'to_port'
+#     from_port = 'from_port'
+
+
+class NodeTaskGraphMixin(object):
+    '''Relies on mixing in with a Node class that has the following attributes
+    and methods:
+        ATTRIBUTES
+        ----------
+            _task_obj
+            uid
+            conf
+            load
+            save
+            delayed_process
+
+            required
+            addition
+            deletion
+            retention
+            rename
+
+        METHODS
+        -------
+            process
+            load_cache
+            save_cache
+            _using_ports
+            _get_input_ports
+            _get_output_ports
+    '''
+
+    def __init__(self):
+        self.inputs = []
+        self.outputs = []
+        self.visited = False
+
+        self.input_df = {}
+        # input_df format:
+        # {
+        #     iport0: df_for_iport0,
+        #     iport1: df_for_iport1,
+        # }
+        # Note: that even though the "df" terminology is used the type is
+        #     user configurable i.e. "df" is just some python object which is
+        #     typically a data container.
+
+        self.input_columns = {}
+        # input_columns format:
+        # {
+        #     iport0: {
+        #         col1_name: col1_type,
+        #         col2_name: col2_type,
+        #         ... etc.
+        #     },
+        #     iport1: { ... }
+        #     ... etc.
+        # }
+
+        # For the input_columns there's a dummy enumerated port for non-ports
+        # API nodes (one can always enumerate the inputs in order) so the
+        # inputs_columns format is always the same. The output_columns will be
+        # different depending on if it's a port based node or non-port.
+
+        self.output_columns = {}
+        # output_columns format when using ports:
+        # {
+        #     oport1: {
+        #         col1_name: col1_type,
+        #         col2_name: col2_type,
+        #         ... etc.
+        #     },
+        #     oport2: { ... }
+        #     ... etc.
+        # }
+        #
+        # output_columns format when not using ports:
+        # {
+        #     col1_name: col1_type,
+        #     col2_name: col2_type,
+        #     ... etc.
+        # }
+
+        self.clear_input = True
+
+    def __translate_column(self, columns):
+        output = {}
+        for col_name, col_type in columns.items():
+            if col_type is not None and col_type.startswith("@"):
+                col_type = self.conf[col_type[1:]]
+            if col_name.startswith("@"):
+                field_name = col_name[1:]
+                v = self.conf[field_name]
+                if isinstance(v, str):
+                    output[v] = col_type
+                elif isinstance(v, list):
+                    for item in v:
+                        output[item] = None
+            else:
+                output[col_name] = col_type
+
+        return output
+
+    def columns_flow(self):
+        """
+        Flow the graph to determine the input output dataframe column names and
+        types.
+        """
+
+        def validate_required(icols, kcol, kval, in_taskid=None, iport=None):
+            if kcol not in icols:
+                err_msg = 'Incoming columns not valid: error for node "%s", '\
+                    'missing required column "%s".' % (self.uid, kcol)
+                if in_taskid:
+                    dst_uid = self.uid if iport is None else \
+                        '{}.{}'.format(self.uid, iport)
+                    err_msg = '{}\nIncoming columns from "{}" do not match '\
+                        'columns_setup for "{}".'.format(
+                            err_msg, in_taskid, dst_uid)
+                raise Exception(err_msg)
+            if kval != icols[kcol]:
+                # special case for 'date'
+                if (kval == 'date' and icols[kcol]
+                   in ('datetime64[ms]', 'date', 'datetime64[ns]')):
+                    # continue
+                    return
+                else:
+                    print("error for node %s, "
+                          "type %s mismatch %s"
+                          % (self.uid, kval, icols[kcol]))
+
+        incols_ready = self.__input_columns_ready()
+        if not incols_ready:
+            return
+
+        inputs_cols = self.__get_input_columns()
+
+        if not self._using_ports():
+            # to_port (iport usually used as variable) is always set. Refer to
+            # TaskGraph.build method. In non-port case inputs are enumerated
+            # in the order that inputs are listed in the task spec. The order
+            # idx is used as an ad-hoc ports that aren't used. Below the data
+            # structure of inputs_cols is flattened.
+            incoming_cols = {
+                col_name: col_type for icol_dict in inputs_cols.values()
+                for col_name, col_type in icol_dict.items()
+            }
+            inputs_cols = incoming_cols
+
+        # check required inpurt columns are there
+        if self.required:
+            required = self.required
+            pinputs = self._task_obj[TaskSpecSchema.inputs]
+            if self._using_ports():
+                for iport in self._get_input_ports():
+                    required_iport = {
+                        col_name: col_type for col_name, col_type in
+                        required.get(iport, {}).items()}
+
+                    required_tran = self.__translate_column(required_iport)
+                    incoming_cols = inputs_cols[iport]
+                    in_taskid = pinputs[iport]
+
+                    for kcol, kval in required_tran.items():
+                        validate_required(incoming_cols, kcol, kval,
+                                          in_taskid, iport)
+            else:
+                # required_flat = required
+                required_tran = self.__translate_column(required)
+                in_taskids = ', '.join(pinputs)
+                for kcol, kval in required_tran.items():
+                    validate_required(incoming_cols, kcol, kval, in_taskids)
+
+        # ABOVE validates the columns in dataframe inputs
+
+        combined = {}
+        # When using ports all the validation logic below add/del/retain
+        # can just be simplified to having a columns dict for the port.
+        # The operations add/del/retain are internal to the process API
+        # of a Node implementation.
+        # Renaming a column is a special case as it is a meta-operation where
+        # a column is renamed dynmically during run-time. The rename is
+        # identified via "@" special character and typically configured via
+        # task-spec conf.
+        if self._using_ports():
+            out_ports = self._get_output_ports()
+            for oport in out_ports:
+                # TODO: Translate needs to be port aware. Assumes
+                #     translation is defined in self.conf:
+                #         types = self.conf[types[1:]]
+                #     The conf should then be ports aware.
+                oport_req_cols_tran = self.__translate_column(
+                    self.required.get(oport, {}))
+                combined[oport] = oport_req_cols_tran
+        else:
+            # old API assumes input columns are passed through
+            combined.update(inputs_cols)
+
+        # compute the output columns
+        output_cols = combined
+
+        if self.addition:
+            if self._using_ports():
+                for oport in out_ports:
+                    add_cols = self.__translate_column(
+                        self.addition.get(oport, {}))
+                    col_dict = output_cols.get(oport, {})
+                    col_dict.update(add_cols)
+                    output_cols[oport] = col_dict
+            else:
+                add_cols = self.__translate_column(self.addition)
+                output_cols.update(add_cols)
+
+        if self.deletion:
+            if self._using_ports():
+                for oport in out_ports:
+                    del_cols = self.__translate_column(
+                        self.deletion.get(oport, {}))
+                    col_dict = output_cols.get(oport, {})
+                    for kdel in del_cols:
+                        del col_dict[kdel]
+                    output_cols[oport] = col_dict
+            else:
+                for kdel in self.__translate_column(self.deletion).keys():
+                    del output_cols[kdel]
+
+        if self.retention is not None:
+            if self._using_ports():
+                for oport in out_ports:
+                    output_cols[oport] = self.__translate_column(
+                        self.retention.get(oport, {}))
+            else:
+                output_cols = self.__translate_column(self.retention)
+
+        def rename_check(kk, cols):
+            if kk not in cols:
+                err_msg = 'Not valid replacement column: error for node "%s",'\
+                          ' missing required column "%s"' % (self.uid, kk)
+                raise Exception(err_msg)
+
+        if self.rename:
+            if self._using_ports():
+                for oport in out_ports:
+                    replacement = self.__translate_column(
+                        self.rename.get(oport, {}))
+                    col_dict = output_cols.get(oport, {})
+                    for col_key, repl_name in replacement.items():
+                        rename_check(col_key, col_dict)
+                        types = col_dict[col_key]
+                        del col_dict[col_key]
+                        col_dict[repl_name] = types
+                    output_cols[oport] = col_dict
+            else:
+                replacement = self.__translate_column(self.rename)
+                for col_key, repl_name in replacement.items():
+                    rename_check(col_key, output_cols)
+                    types = output_cols[col_key]
+                    del output_cols[col_key]
+                    output_cols[repl_name] = types
+
+        self.output_columns = output_cols
+
+        for iout in self.outputs:
+            onode = iout['to_node']
+            iport = iout['to_port']
+            oport = iout['from_port']
+#             onode.__set_input_column(self, self.output_columns)
+            if oport is not None:
+                out_cols = self.output_columns[oport]
+            else:
+                if self._using_ports():
+                    # oport is not specified but this is a port based Node.
+                    # That means it is outputing to a non-port based Node.
+                    # Flattening output_columns across all output ports:
+                    #     COMPATIBILITY FOR NON-PORT API NODES
+                    out_cols = {
+                        col_name: col_type
+                        for col_dict in self.output_columns.values()
+                        for col_name, col_type in col_dict.items()}
+                else:
+                    out_cols = self.output_columns
+            onode.__set_input_column(iport, out_cols)
+            onode.columns_flow()
+
+    def _validate_df(self, df_to_val, ref_cols):
+        '''Validate a cudf or dask_cudf DataFrame.
+
+        :param df_to_val: A dataframe typically of type cudf.DataFrame or
+            dask_cudf.DataFrame.
+        :param ref_cols: Dictionary of column names and their expected types.
+        :returns: True or False based on matching all columns in the df_to_val
+            and columns spec in ref_cols.
+        :raises: Exception - Raised when invalid dataframe length or unexpected
+            number of columns. TODO: Create a ValidationError subclass.
+
+        '''
+        if (isinstance(df_to_val, cudf.DataFrame) or
+            isinstance(df_to_val, dask_cudf.DataFrame)) and \
+                len(df_to_val) == 0:
+            err_msg = 'Node "{}" produced empty output'.format(self.uid)
+            raise Exception(err_msg)
+
+        if not isinstance(df_to_val, cudf.DataFrame) and \
+           not isinstance(df_to_val, dask_cudf.DataFrame):
+            return True
+
+        i_cols = df_to_val.columns
+        if len(i_cols) != len(ref_cols):
+            print("expect %d columns, only see %d columns"
+                  % (len(ref_cols), len(i_cols)))
+            print("ref:", ref_cols)
+            print("columns", i_cols)
+            raise Exception("not valid for node %s" % (self.uid))
+
+        for col in ref_cols.keys():
+            if col not in i_cols:
+                print("error for node %s, column %s is not in the required "
+                      "output df" % (self.uid, col))
+                return False
+
+            if ref_cols[col] is None:
+                continue
+
+            err_msg = "for node {} type {}, column {} type {} "\
+                "does not match expected type {}".format(
+                    self.uid, type(self), col, df_to_val[col].dtype,
+                    ref_cols[col])
+
+            if ref_cols[col] == 'category':
+                # comparing pandas.core.dtypes.dtypes.CategoricalDtype to
+                # numpy.dtype causes TypeError. Instead, let's compare
+                # after converting all types to their string representation
+                # d_type_tuple = (pd.core.dtypes.dtypes.CategoricalDtype(),)
+                d_type_tuple = (str(pd.CategoricalDtype()),)
+            elif ref_cols[col] == 'date':
+                # Cudf read_csv doesn't understand 'datetime64[ms]' even
+                # though it reads the data in as 'datetime64[ms]', but
+                # expects 'date' as dtype specified passed to read_csv.
+                d_type_tuple = ('datetime64[ms]', 'date', 'datetime64[ns]')
+            else:
+                d_type_tuple = (str(np.dtype(ref_cols[col])),)
+
+            if (str(df_to_val[col].dtype) not in d_type_tuple):
+                print("ERROR: {}".format(err_msg))
+                # Maybe raise an exception here and have the caller
+                # try/except the validation routine.
+                return False
+
+        return True
+
+    def __valide(self, node_output, ref_cols):
+        if self._using_ports():
+            # Validate each port
+            out_ports = self._get_output_ports(full_port_spec=True)
+            for pname, pspec in out_ports.items():
+                out_optional = pspec.get('optional', False)
+                if pname not in node_output:
+                    if out_optional:
+                        continue
+                    else:
+                        raise Exception('Node "{}" did not produce output "{}"'
+                                        .format(self.uid, pname))
+
+                out_val = node_output[pname]
+                out_type = type(out_val)
+
+                expected_type = pspec.get(PortsSpecSchema.port_type)
+                if expected_type:
+                    if not isinstance(expected_type, list):
+                        expected_type = [expected_type]
+
+                    if self.delayed_process and \
+                            cudf.DataFrame in expected_type and \
+                            dask_cudf.DataFrame not in expected_type:
+                        expected_type.extend([dask_cudf.DataFrame])
+
+                    if out_type not in expected_type:
+                        raise Exception(
+                            'Node "{}" output port "{}" produced wrong type '
+                            '"{}". Expected type "{}"'
+                            .format(self.uid, pname, out_type, expected_type))
+
+                cudf_types_tuple = (cudf.DataFrame, dask_cudf.DataFrame)
+
+                if out_type in cudf_types_tuple:
+                    if len(out_val) == 0 and out_optional:
+                        continue
+
+                if out_type in cudf_types_tuple:
+                    cols_to_val = ref_cols.get(pname)
+                    val_flag = self._validate_df(out_val, cols_to_val)
+                    if not val_flag:
+                        raise Exception("not valid output")
+        else:
+            val_flag = self._validate_df(node_output, ref_cols)
+
+            if not val_flag:
+                raise Exception("not valid output")
+
+    def __input_ready(self):
+        if not isinstance(self.load, bool) or self.load:
+            return True
+
+        for ient in self.inputs:
+            iport = ient['to_port']
+
+            if iport not in self.input_df:
+                return False
+
+        return True
+
+    def __input_columns_ready(self):
+        for ii in self.inputs:
+            iport = ii['to_port']
+
+            if iport not in self.input_columns:
+                return False
+
+        return True
+
+    def __get_input_df(self):
+        return self.input_df
+
+    def __get_input_columns(self):
+        return self.input_columns
+
+    def __set_input_df(self, to_port, df):
+        self.input_df[to_port] = df
+
+    def __set_input_column(self, to_port, columns):
+        self.input_columns[to_port] = columns
+
+    def flow(self):
+        """
+        flow from this node to do computation.
+            * it will check all the input dataframe are ready or not
+            * calls its process function to manipulate the input dataframes
+            * set the resulting dataframe to the children nodes as inputs
+            * flow each of the chidren nodes
+        """
+        input_ready = self.__input_ready()
+        if not input_ready:
+            return
+
+        inputs_data = self.__get_input_df()
+        output_df = self.__call__(inputs_data)
+
+        self_has_ports = self._using_ports()
+
+        if self.clear_input:
+            self.input_df = {}
+
+        for out in self.outputs:
+            onode = out['to_node']
+            iport = out['to_port']
+            oport = out['from_port']
+
+            onode_has_ports = onode._using_ports()
+
+            if oport is not None:
+                if oport not in output_df:
+                    if onode.uid in (OUTPUT_ID,):
+                        onode_msg = 'is listed in task-graph outputs'
+                    else:
+                        onode_msg = 'is required as input to node "{}"'.format(
+                            onode.uid)
+                    err_msg = 'ERROR: Missing output port "{}" from '\
+                        'node "{}". This output {}.'.format(
+                            oport, self.uid, onode_msg)
+                    raise Exception(err_msg)
+                df = output_df[oport]
+            else:
+                if self_has_ports and not onode_has_ports:
+                    # Unpack for convenience when passing data from nodes with
+                    # ports to nodes without ports. If in the future will
+                    # convert to a ports only API then clean up this code.
+                    output_list = list(output_df.values())
+                    if len(output_list) == 1:
+                        output_unpack = output_list[0]
+                    else:
+                        output_unpack = [self.__make_copy(data_input)
+                                         for data_input in output_list]
+
+                    df = output_unpack
+                else:
+                    df = output_df
+
+            onode.__set_input_df(iport, df)
+
+            onode.flow()
+
+    def __make_copy(self, df_obj):
+        if isinstance(df_obj, cudf.DataFrame):
+            return df_obj.copy(deep=False)
+        elif isinstance(df_obj, dask_cudf.DataFrame):
+            # TODO: This just makes a df_obj with a shallow copy of the
+            #     underlying computational graph. It does not affect the
+            #     underlying data. Why is a copy of dask graph needed?
+            return df_obj.copy()
+        else:
+            return df_obj
+
+    def __check_dly_processing_prereq(self, inputs):
+        '''All inputs must be dask_cudf.DataFrame types. Output types must
+        be specified as cudf.DataFrame or dask_cudf.DataFrame. (Functionality
+        could also be extended to support dask.dataframe.DataFrame, but
+        currently only cudf/dask_cudf dataframes are supported.)
+        '''
+        # check if dask future or delayed
+        use_delayed = False
+        in_types = {}
+        for iport, ival in inputs.items():
+            itype = type(ival)
+            in_types[iport] = itype
+            if itype in (dask_cudf.DataFrame,):
+                use_delayed = True
+
+        if use_delayed:
+            warn_msg = \
+                'Node "{}" iport "{}" is of type "{}" and it '\
+                'should be dask_cudf.DataFrame. Ignoring '\
+                '"delayed_process" setting.'
+            for iport, itype in in_types.items():
+                if itype not in (dask_cudf.DataFrame,):
+                    warnings.warn(warn_msg.format(self.uid, iport, itype))
+                    use_delayed = False
+
+        if use_delayed:
+            warn_msg = \
+                'Node "{}" oport "{}" is of type "{}" and it '\
+                'should be cudf.DataFrame or dask_cudf.DataFrame. Ignoring '\
+                '"delayed_process" setting.'
+            for oport, oport_spec in \
+                    self._get_output_ports(full_port_spec=True).items():
+                otype = oport_spec.get('type', [])
+                if not isinstance(otype, list):
+                    otype = [otype]
+                if dask_cudf.DataFrame not in otype and \
+                        cudf.DataFrame not in otype:
+                    warnings.warn(warn_msg.format(self.uid, oport, otype))
+                    use_delayed = False
+
+        return use_delayed
+
+    def __delayed_call(self, inputs):
+        '''Delayed processing called when self.delayed_process is set. To
+        handle delayed processing automatically, prerequisites are checked via
+        call to:
+            :meth:`__check_dly_processing_prereq`
+        Additionally all input dask_cudf dataframes have to be partitioned
+        the same i.e. equal number of partitions.
+        '''
+
+        def get_pout(out_dict, port):
+            '''Get the output in out_dict at key port. Used for delayed
+            unpacking.'''
+            # DEBUGGING
+            # try:
+            #     from dask.distributed import get_worker
+            #     worker = get_worker()
+            #     print('worker{} get_pout NODE "{}" port "{}" worker: {}'
+            #           .format(worker.name, self.uid, port, worker))
+            # except Exception as err:
+            #     print(err)
+
+            df_out = out_dict.get(port, cudf.DataFrame())
+
+            if isinstance(df_out, cudf.DataFrame):
+                # Needed for the same reason as __make_copy. To prevent columns
+                # addition in the input data frames. In python everything is
+                # by reference value and dataframes are mutable.
+                # Handle the case when dask_cudf.DataFrames are source frames
+                # which appear as cudf.DataFrame in a dask-delayed function.
+                return df_out.copy(deep=False)
+
+            return df_out
+
+        inputs_dly = {}
+        # A dask_cudf object will return a list of dask delayed object using
+        # to_delayed() API. Below the logic assumes (otherwise error) that
+        # all inputs are dask_cudf objects and are distributed in the same
+        # manner. Ex. inputs_dly:
+        #     inputs_dly = {
+        #         p0: {
+        #             iport0: ddf_dly_i0_p0,
+        #             iport1: ddf_dly_i1_p0,
+        #             ... for all iports
+        #         },
+        #         p1: {
+        #             iport0: ddf_dly_i0_p1,
+        #             iport1: ddf_dly_i1_p1,
+        #             ... for all iports
+        #         },
+        #         ... for all partitions
+        # i_x - iport
+        # p_x - partition index
+
+        npartitions = None
+        for iport, dcudf in inputs.items():
+            ddf_dly_list = dcudf.to_delayed()
+            npartitions_ = len(ddf_dly_list)
+            if npartitions is None:
+                npartitions = npartitions_
+            if npartitions != npartitions_:
+                raise Exception(
+                    'Error DASK_CUDF PARTITIONS MISMATCH: Node "{}" input "{}"'
+                    ' has {} npartitions and other inputs have {} partitions'
+                    .format(self.uid, iport, npartitions_, npartitions))
+            for idly, dly in enumerate(ddf_dly_list):
+                inputs_dly.setdefault(idly, {}).update({
+                    # iport: dly.persist()  # DON'T PERSIST HERE
+                    iport: dly
+                })
+
+        # DEBUGGING
+        # print('INPUTS_DLY:\n{}'.format(inputs_dly))
+
+        outputs_dly = {}
+        # Formulate a list of delayed objects for each output port to be able
+        # to call from_delayed to synthesize a dask_cudf object.
+        # Ex. outputs_dly:
+        #     outputs_dly = {
+        #         o0: [ddf_dly_o0_p0, ddf_dly_o0_p1, ... _pN]
+        #         o1: [ddf_dly_o1_p0, ddf_dly_o1_p1, ... _pN]
+        #         ... for all output ports
+        #     }
+        # o_x - output port
+        # p_x - delayed partition
+
+        # VERY IMPORTANT TO USE PERSIST:
+        # https://docs.dask.org/en/latest/dataframe-api.html#dask.dataframe.DataFrame.persist
+        # Otherwise process will run several times.
+        for inputs_ in inputs_dly.values():
+            output_df_dly = dask.delayed(self.process)(inputs_)
+            output_df_dly_per = output_df_dly.persist()
+            for oport in self._get_output_ports():
+                oport_out = dask.delayed(get_pout)(
+                    output_df_dly_per, oport)
+                outputs_dly.setdefault(oport, []).append(oport_out.persist())
+
+        # DEBUGGING
+        # print('OUTPUTS_DLY:\n{}'.format(outputs_dly))
+
+        output_df = {}
+        # A dask_cudf object is synthesized from a list of delayed objects.
+        # Per outputs_dly above use dask_cudf.from_delayed API.
+        for oport in self._get_output_ports():
+            output_df[oport] = dask_cudf.from_delayed(outputs_dly[oport])
+
+        return output_df
+
+    def __call__(self, inputs_data):
+        if self._using_ports():
+            # nodes with ports take dictionary as inputs
+            inputs = {iport: self.__make_copy(data_input)
+                      for iport, data_input in inputs_data.items()}
+        else:
+            # nodes without ports take list as inputs
+            inputs = [self.__make_copy(data_input)
+                      for data_input in inputs_data.values()]
+
+        if self.load:
+            if isinstance(self.load, bool):
+                output_df = self.load_cache()
+            else:
+                output_df = self.load
+        else:
+            if not self.delayed_process:
+                output_df = self.process(inputs)
+            else:
+                if self._using_ports():
+                    use_delayed = self.__check_dly_processing_prereq(inputs)
+                    if use_delayed:
+                        output_df = self.__delayed_call(inputs)
+                    else:
+                        output_df = self.process(inputs)
+                else:
+                    # handle the dask dataframe automatically
+                    # use the to_delayed interface
+                    # TODO, currently only handles first input is dask_cudf df
+                    i_df = inputs[0]
+                    rest = inputs[1:]
+                    if isinstance(i_df, dask_cudf.DataFrame):
+                        d_fun = dask.delayed(self.process)
+                        output_df = dask_cudf.from_delayed([
+                            d_fun([item] + rest)
+                            for item in i_df.to_delayed()])
+                    else:
+                        output_df = self.process(inputs)
+
+        if self.uid != OUTPUT_ID and output_df is None:
+            raise Exception("None output")
+        else:
+            self.__valide(output_df, self.output_columns)
+
+        if self.save:
+            self.save_cache(output_df)
+
+        return output_df
diff --git a/gquant/dataframe_flow/node.py b/gquant/dataframe_flow/node.py
index aa7bf874..50093b1e 100644
--- a/gquant/dataframe_flow/node.py
+++ b/gquant/dataframe_flow/node.py
@@ -1,24 +1,99 @@
-import abc
-import numpy as np
 import os
-import cudf
+import warnings
+import abc
 import pandas as pd
-import dask_cudf
-import dask
+import cudf
+
+from .task import Task
+from .taskSpecSchema import TaskSpecSchema
+from .portsSpecSchema import PortsSpecSchema
+
+from ._node import _Node
+
+
+__all__ = ['Node']
+
+
+class _PortsMixin(object):
+    '''Mixed class must have (doesn't have to implement i.e. relies on
+    NotImplementedError) "ports_setup" method otherwise raises AttributeError.
+    '''
+    def _using_ports(self):
+        '''Check if the :meth:`ports_setup` is implemented. If it is return
+        True otherwise return False i.e. ports API or no-ports API.
+        '''
+        try:
+            _ = self.ports_setup()
+            has_ports = True
+        except NotImplementedError:
+            has_ports = False
+        return has_ports
+
+    def __get_io_port(self, io=None, full_port_spec=False):
+        input_ports, output_ports = self.ports_setup()
+        if io in ('in',):
+            io_ports = input_ports
+        else:
+            io_ports = output_ports
+
+        if io_ports is None:
+            io_ports = dict()
+
+        if not full_port_spec:
+            io_ports = list(io_ports.keys())
+
+        return io_ports
 
-OUTPUT_ID = 'f291b900-bd19-11e9-aca3-a81e84f29b0f_uni_output'
+    def _get_input_ports(self, full_port_spec=False):
+        return self.__get_io_port(io='in', full_port_spec=full_port_spec)
 
-__all__ = ['Node', 'OUTPUT_ID']
+    def _get_output_ports(self, full_port_spec=False):
+        return self.__get_io_port(io='out', full_port_spec=full_port_spec)
 
 
-class Node(object):
-    __metaclass__ = abc.ABCMeta
+class Node(_PortsMixin, _Node):
+    '''Base class for implementing gQuant plugins i.e. nodes. A node processes
+    tasks within a gQuant task graph.
 
-    cache_dir = os.getenv('GQUANT_CACHE_DIR', ".cache")
+    If one desires to use ports API then must implement the following method:
+
+        :meth: ports_setup
+            Defines ports for the node. Refer to ports_setup docstring for
+            further details.
+
+    A node implementation must override the following methods:
+
+        :meth: columns_setup
+            Define expected columns in dataframe processing. If
+            inputs/outputs are not dataframes then implement a pass through
+            without details. Ex.:
+                def columns_setup(self):
+                    pass
+            When processing dataframes define expected columns. Ex.:
+                # non-port API
+                def columns_setup(self):
+                    self.required = {'x': 'float64',
+                                     'y': 'float64'}
+
+                # ports API
+                def columns_setup(self):
+                    self.required = {
+                        'iport0_name': {'x': 'float64',
+                                        'y': 'float64'}
+                        'iport1_name': some_dict,
+                        etc.
+                    }
+            Refer to columns_setup docstring for further details.
+
+        :meth: process
+            Main functionaliy or processing logic of the Node. Refer to
+            process docstring for further details.
+
+    '''
+
+    cache_dir = '.cache'
 
     def __init__(self, task):
-        from .taskSpecSchema import TaskSpecSchema
-        from .task import Task
         # make sure is is a task object
         assert isinstance(task, Task)
         self._task_obj = task  # save the task obj
@@ -26,39 +101,64 @@ def __init__(self, task):
         self.conf = task[TaskSpecSchema.conf]
         self.load = task.get(TaskSpecSchema.load, False)
         self.save = task.get(TaskSpecSchema.save, False)
-        self.inputs = []
-        self.outputs = []
-        self.visited = False
-        self.input_df = {}
-        self.input_columns = {}
-        self.output_columns = {}
-        self.clear_input = True
-        self.required = None
-        self.addition = None
-        self.deletion = None
+
+        self.required = {}
+        self.addition = {}
+        self.deletion = {}
+        # Retention must be None instead of empty dict. This replaces anything
+        # set by required/addition/retention. An empty dict is a valid setting
+        # for retention therefore use None instead of empty dict.
         self.retention = None
-        self.rename = None
+        self.rename = {}
         self.delayed_process = False
         # customized the column setup
         self.columns_setup()
 
-    def __translate_column(self, columns):
-        output = {}
-        for k in columns:
-            types = columns[k]
-            if types is not None and types.startswith("@"):
-                types = self.conf[types[1:]]
-            if k.startswith("@"):
-                field_name = k[1:]
-                v = self.conf[field_name]
-                if isinstance(v, str):
-                    output[v] = types
-                elif isinstance(v, list):
-                    for item in v:
-                        output[item] = None
-            else:
-                output[k] = types
-        return output
+        if self._using_ports():
+            PortsSpecSchema.validate_ports(self.ports_setup())
+
+    def ports_setup(self):
+        """Virtual method for specifying inputs/outputs ports. Implement if
+        desire to use ports API for Nodes in a TaskGraph. Leave un-implemented
+        for non-ports API.
+
+        Must return an instance of NodePorts that adheres to PortsSpecSchema.
+        Refer to PortsSpecSchema and NodePorts in module:
+            gquant.dataframe_flow.portsSpecSchema
+
+        Ex. empty no-ports but still implement ports API.
+            node_ports = NodePorts()
+            return node_ports
+
+        Ex. ports for inputs and outputs. (typical case)
+            inports = {
+                'iport0_name': {
+                    PortsSpecSchema.port_type: cudf.DataFrame
+                },
+                'iport1_name': {
+                    PortsSpecSchema.port_type: cudf.DataFrame,
+                    PortsSpecSchema.optional: True
+                }
+            }
+
+            outports = {
+                'oport0_name': {
+                    PortsSpecSchema.port_type: cudf.DataFrame
+                },
+                'oport1_name': {
+                    PortsSpecSchema.port_type: cudf.DataFrame,
+                    PortsSpecSchema.optional: True
+                }
+            }
+
+            node_ports = NodePorts(inports=inports, outports=outports)
+            return node_ports
+
+        :return: Node ports
+        :rtype: NodePorts
+
+        """
+        raise NotImplementedError
 
     @abc.abstractmethod
     def columns_setup(self):
@@ -99,231 +199,14 @@ def columns_setup(self):
         `self.conf` variable.
 
         """
-        self.required = None
-        self.addition = None
-        self.deletion = None
+        self.required = {}
+        self.addition = {}
+        self.deletion = {}
+        # Retention must be None instead of empty dict. This replaces anything
+        # set by required/addition/retention. An empty dict is a valid setting
+        # for retention therefore use None instead of empty dict.
         self.retention = None
-        self.rename = None
-
-    def columns_flow(self):
-        """
-        Flow the graph to determine the input output dataframe column names and
-        types.
-        """
-        if not self.__input_columns_ready():
-            return
-        inputs = self.__get_input_columns()
-
-        # check required columns are their
-        if self.required is not None:
-            required = self.__translate_column(self.required)
-            for i in inputs:
-                for k in required:
-                    if k not in i:
-                        print("error for node %s, "
-                              "missing required column %s" % (self.uid, k))
-                        raise Exception("not valid input")
-                    if required[k] != i[k]:
-                        # special case for 'date'
-                        if (required[k] == 'date' and i[k]
-                           in ('datetime64[ms]', 'date', 'datetime64[ns]')):
-                            continue
-                        else:
-                            print("error for node %s, "
-                                  "type %s mismatch %s"
-                                  % (self.uid, required[k], i[k]))
-
-        combined = {}
-        for i in inputs:
-            combined.update(i)
-
-        # compute the output columns
-        output = combined
-        if self.addition is not None:
-            output.update(self.__translate_column(self.addition))
-        if self.deletion is not None:
-            for key in self.__translate_column(self.deletion).keys():
-                del output[key]
-        if self.retention is not None:
-            output = self.__translate_column(self.retention)
-        if self.rename is not None:
-            replacement = self.__translate_column(self.rename)
-            for key in replacement.keys():
-                if key not in output:
-                    print("error for node %s, "
-                          "missing required column %s" % (self.uid, key))
-                    raise Exception("not valid replacement column")
-                types = output[key]
-                del output[key]
-                output[replacement[key]] = types
-        self.output_columns = output
-        for o in self.outputs:
-            o.__set_input_column(self, self.output_columns)
-            o.columns_flow()
-
-    def __valide(self, input_df, ref):
-        if not isinstance(input_df, cudf.DataFrame) and \
-           not isinstance(input_df, dask_cudf.DataFrame):
-            return True
-
-        i_cols = input_df.columns
-        if len(i_cols) != len(ref):
-            print("expect %d columns, only see %d columns"
-                  % (len(ref), len(i_cols)))
-            print("ref:", ref)
-            print("columns", i_cols)
-            raise Exception("not valid for node %s" % (self.uid))
-
-        for col in ref.keys():
-            if col not in i_cols:
-                print("error for node %s, %s is not in the required input df"
-                      % (self.uid, col))
-                return False
-
-            if ref[col] is None:
-                continue
-
-            err_msg = "for node {} type {}, column {} type {} "\
-                "does not match expected type {}".format(
-                    self.uid, type(self), col, input_df[col].dtype,
-                    ref[col])
-
-            if ref[col] == 'category':
-                # comparing pandas.core.dtypes.dtypes.CategoricalDtype to
-                # numpy.dtype causes TypeError. Instead, let's compare
-                # after converting all types to their string representation
-                # d_type_tuple = (pd.core.dtypes.dtypes.CategoricalDtype(),)
-                d_type_tuple = (str(pd.core.dtypes.dtypes.CategoricalDtype()),)
-            elif ref[col] == 'date':
-                # Cudf read_csv doesn't understand 'datetime64[ms]' even
-                # though it reads the data in as 'datetime64[ms]', but
-                # expects 'date' as dtype specified passed to read_csv.
-                d_type_tuple = ('datetime64[ms]', 'date', 'datetime64[ns]')
-            else:
-                d_type_tuple = (str(np.dtype(ref[col])),)
-
-            if (str(input_df[col].dtype) not in d_type_tuple):
-                print("ERROR: {}".format(err_msg))
-                # Maybe raise an exception here and have the caller
-                # try/except the validation routine.
-                return False
-
-        return True
-
-    def __input_ready(self):
-        if not isinstance(self.load, bool) or self.load:
-            return True
-        for i in self.inputs:
-            if i not in self.input_df:
-                return False
-        return True
-
-    def __input_columns_ready(self):
-        for i in self.inputs:
-            if i not in self.input_columns:
-                return False
-        return True
-
-    def __get_input_df(self):
-        input_df = []
-        if not isinstance(self.load, bool) or self.load:
-            return input_df
-        for i in self.inputs:
-            input_df.append(self.input_df[i])
-        return input_df
-
-    def __get_input_columns(self):
-        input_columns = []
-        for i in self.inputs:
-            input_columns.append(self.input_columns[i])
-        return input_columns
-
-    def __set_input_df(self, parent, df):
-        self.input_df[parent] = df
-
-    def __set_input_column(self, parent, columns):
-        self.input_columns[parent] = columns
-
-    def flow(self):
-        """
-        flow from this node to do computation.
-            * it will check all the input dataframe are ready or not
-            * calls its process function to manipulate the input dataframes
-            * set the resulting dataframe to the children nodes as inputs
-            * flow each of the chidren nodes
-        """
-        if not self.__input_ready():
-            return
-        inputs = self.__get_input_df()
-        output_df = self.__call__(inputs)
-        if self.clear_input:
-            self.input_df = {}
-        for o in self.outputs:
-            o.__set_input_df(self, output_df)
-            o.flow()
-
-    def __make_copy(self, i):
-        if isinstance(i, cudf.DataFrame):
-            return i.copy(deep=False)
-        elif isinstance(i, dask_cudf.DataFrame):
-            return i.copy()
-        else:
-            return i
-
-    def load_cache(self, filename):
-        """
-        defines the behavior of how to load the cache file from the `filename`.
-        Node can override this method.
-
-        Arguments
-        -------
-        filename: str
-            filename of the cache file
-
-        """
-        output_df = cudf.read_hdf(filename, key=self.uid)
-        return output_df
-
-    def __call__(self, inputs):
-        # valide inputs
-        Class = type(self)
-        cache = Class.cache_dir
-        inputs = [self.__make_copy(i) for i in inputs]
-        if not isinstance(self.load, bool) or self.load:
-            if isinstance(self.load, bool):
-                output_df = self.load_cache(cache+'/'+self.uid+'.hdf5')
-            else:
-                output_df = self.load
-        else:
-            if not self.delayed_process:
-                output_df = self.process(inputs)
-            else:
-                # handle the dask dataframe automatically
-                # use the to_delayed interface
-                # TODO, currently only handles first input is dask_cudf df
-                i_df = inputs[0]
-                rest = inputs[1:]
-                if isinstance(i_df,  dask_cudf.DataFrame):
-                    d_fun = dask.delayed(self.process)
-                    output_df = dask_cudf.from_delayed([
-                        d_fun([item] + rest) for item in i_df.to_delayed()])
-                else:
-                    output_df = self.process(inputs)
-
-        if self.uid != OUTPUT_ID and output_df is None:
-            raise Exception("None output")
-        elif (isinstance(output_df, cudf.DataFrame) or
-              isinstance(output_df, dask_cudf.DataFrame)
-              ) and len(output_df) == 0:
-            raise Exception("empty output")
-        elif not self.__valide(output_df, self.output_columns):
-            raise Exception("not valid output")
-
-        if self.save:
-            os.makedirs(cache, exist_ok=True)
-            output_df.to_hdf(cache+'/'+self.uid+'.hdf5', key=self.uid)
-
-        return output_df
+        self.rename = {}
 
     @abc.abstractmethod
     def process(self, inputs):
@@ -333,12 +216,141 @@ def process(self, inputs):
 
         Arguments
         -------
-         inputs: list
-            list of input dataframes. dataframes order in the list matters
+        inputs: list or dictionary
+            Depending on if ports_setup is implemented or not i.e. ports API
+            or no-ports API, the inputs is a list (no ports API) or a
+            dictionary (ports API).
+            NO PORTS:
+                list of input dataframes. dataframes order in the list matters
+                Ex: inputs = [df0, df1, df2, etc.]
+                Within the context of connected nodes in a task-graph a
+                task spec specifies inputs as a list of task-ids of input
+                tasks. During task-graph run the inputs setup for process
+                will be a list of outputs from those tasks (corresponding
+                to task-spec task-ids ) in the order set in the task-spec.
+                Ex.:
+                    TaskSpecSchema.inputs: [
+                        some_task_id,
+                        some_other_task_id,
+                        etc.
+                    ]
+                Within the process access the dataframes (data inputs) as:
+                    df0 = inputs[0] # from some_task_id
+                    df1 = inputs[1] # from some_other_task_id
+                    etc.
+            PORTS:
+                dictionary keyed by port name as defined in ports_setup.
+                Ex.:
+                    inputs = {
+                        iport0: df0,
+                        iport1: df1,
+                        etc.
+                    }
+                The difference with no-ports case is that the task-spec
+                for inputs is a dictionary keyed by port names with values
+                being task-ids of input tasks "." port output of the input
+                tasks. Ex.:
+                    TaskSpecSchema.inputs: {
+                        iport0: some_task_id.some_oport,
+                        iport1: some_other_task_id.some_oport,
+                        etc.
+                    }
+                Within the process access the dataframes (data inputs) as:
+                    df0 = inputs[iport0] # from some_task_id some_oport
+                    df1 = inputs[iport1] # from some_other_task_id some_oport
+                    etc.
+
         Returns
         -------
         dataframe
-            the processed dataframe
+            The output can be anything representable in python. Typically it's
+            a processed dataframe.
+            NO PORTS:
+                Return some dataframe or output. Ex.:
+                    df = cudf.DataFrame()  # or maybe it can from an input
+                    # do some calculations and populate df.
+                    return df
+            PORTS:
+                Mostly the same as NO PORTS but must return a dictionary keyed
+                by output ports (as defined in ports_setup). Ex.:
+                    df = cudf.DataFrame()  # or maybe it can from an input
+                    # do some calculations and populate df.
+                    return {oport: df}
         """
         output = None
         return output
+
+    def load_cache(self, filename=None):
+        """
+        Defines the behavior of how to load the cache file from the `filename`.
+        Node can override this method. Default implementation assumes cudf
+        dataframes.
+
+        Arguments
+        -------
+        filename: str
+            filename of the cache file. Leave as none to use default.
+
+        """
+        cache_dir = os.getenv('GQUANT_CACHE_DIR', self.cache_dir)
+        if filename is None:
+            filename = cache_dir + '/' + self.uid + '.hdf5'
+
+        if self._using_ports():
+            output_df = {}
+            with pd.HDFStore(filename, mode='r') as hf:
+                for oport, pspec in \
+                        self._get_output_ports(full_port_spec=True).items():
+                    ptype = pspec.get(PortsSpecSchema.port_type)
+                    ptype = [ptype] if not isinstance(ptype, list) else ptype
+                    key = '{}/{}'.format(self.uid, oport)
+                    # check hdf store for the key
+                    if key not in hf:
+                        raise Exception(
+                            'The task "{}" port "{}" key "{}" not found in '
+                            'the hdf file "{}". Cannot load from cache.'
+                            .format(self.uid, oport, filename)
+                        )
+                    if cudf.DataFrame not in ptype:
+                        warnings.warn(
+                            RuntimeWarning,
+                            'Task "{}" port "{}" port type is not set to '
+                            'cudf.DataFrame. Attempting to load port data '
+                            'with cudf.read_hdf.'.format(self.uid, oport))
+                    output_df[oport] = cudf.read_hdf(hf, key)
+        else:
+            output_df = cudf.read_hdf(filename, key=self.uid)
+
+        return output_df
+
+    def save_cache(self, output_df):
+        '''Defines the behavior for how to save the output of a node to
+        filesystem cache. Default implementation assumes cudf dataframes.
+
+        :param output_df: The output from :meth:`process`. For saving to hdf
+            requires that the dataframe(s) have `to_hdf` method.
+        '''
+        cache_dir = os.getenv('GQUANT_CACHE_DIR', self.cache_dir)
+        os.makedirs(cache_dir, exist_ok=True)
+        filename = cache_dir + '/' + self.uid + '.hdf5'
+        if self._using_ports():
+            with pd.HDFStore(filename, mode='w') as hf:
+                for oport, odf in output_df.items():
+                    # check for to_hdf attribute
+                    if not hasattr(odf, 'to_hdf'):
+                        raise Exception(
+                            'Task "{}" port "{}" output object is missing '
+                            '"to_hdf" attribute. Cannot save to cache.'
+                            .format(self.uid, oport))
+
+                    dtype = '{}'.format(type(odf)).lower()
+                    if 'dataframe' not in dtype:
+                        warnings.warn(
+                            RuntimeWarning,
+                            'Task "{}" port "{}" port type is not a dataframe.'
+                            ' Attempting to save to hdf with "to_hdf" method.'
+                            .format(self.uid, oport))
+                    key = '{}/{}'.format(self.uid, oport)
+                    odf.to_hdf(hf, key, format='table', data_columns=True)
+        else:
+            output_df.to_hdf(filename, key=self.uid)
diff --git a/gquant/dataframe_flow/portsSpecSchema.py b/gquant/dataframe_flow/portsSpecSchema.py
new file mode 100644
index 00000000..e57fac2c
--- /dev/null
+++ b/gquant/dataframe_flow/portsSpecSchema.py
@@ -0,0 +1,128 @@
+from collections import Mapping
+from itertools import chain
+from typing import Iterable
+
+from gquant._common import _namedtuple_with_defaults
+
+__all__ = ['PortsSpecSchema', 'NodePorts']
+
+
+_NodePorts = _namedtuple_with_defaults(
+    '_NodePorts',
+    ['inports', 'outports'],
+    {'inports': dict(), 'outports': dict()}
+)
+
+
+class NodePorts(_NodePorts):
+    '''Node ports must be defined for inputs and outputs.
+
+    :ivar inports: Dictionary defining port specs for input ports
+    :ivar outports: Dictionary defining port specs for output ports
+
+    Empty dicts default:
+        node_ports = NodePorts()
+        node_ports.inports and node_ports.outports are empty dicts
+
+    Example with port specs:
+        inports = {
+            'iport0_name': {
+                PortsSpecSchema.port_type: cudf.DataFrame
+            },
+            'iport1_name': {
+                PortsSpecSchema.port_type: cudf.DataFrame,
+                PortsSpecSchema.optional: True
+            }
+        }
+
+        outports = {
+            'oport0_name': {
+                PortsSpecSchema.port_type: cudf.DataFrame
+            },
+            'oport1_name': {
+                PortsSpecSchema.port_type: cudf.DataFrame,
+                PortsSpecSchema.optional: True
+            }
+        }
+
+        node_ports = NodePorts(inports=inports, outports=outports)
+
+    The inports/outports are nested dictionaries. The outer dictionary is keyed
+    by port name with port spec being the value of the outer dictionary. The
+    port spec is a dictionary with keys/fields per PortsSpecSchema class.
+
+    '''
+
+
+class PortsSpecSchema(object):
+    '''Outline fields expected in a ports definition for a node implementation.
+
+    :cvar type: The type of instance for the port. This can also be a
+        list of types if inputs can be of multiple types. Ex.:
+            [cudf.DataFrame, pd.DataFrame]
+        Optional port setting.
+        Default: [] Empty list.
+    :cvar optional: Boolean to indicate whether a given port is optional i.e.
+        the input or output might be optional so missing.
+        Optional port setting.
+        Default: False i.e. if port defined it is assumed required.
+
+    '''
+
+    port_type = 'type'
+    optional = 'optional'
+
+    @classmethod
+    def _typecheck(cls, schema_field, value):
+        if (schema_field == cls.port_type):
+            def check_ptype(val):
+                err_msg = 'Port type must be a pythonic '\
+                    'type i.e type(port_type) == type. Instead got: {}'
+                assert isinstance(val, type), err_msg.format(type(val))
+            if isinstance(value, Iterable):
+                for ptype in value:
+                    check_ptype(ptype)
+            else:
+                check_ptype(value)
+        elif schema_field == cls.optional:
+            assert isinstance(value, bool), 'Optional field must be a '\
+                'boolean. Instead got: {}'.format(value)
+        else:
+            raise KeyError('Uknown schema field "{}" in the port spec.'.format(
+                schema_field))
+
+    # _schema_req_fields = []
+
+    @classmethod
+    def validate_ports(cls, node_ports):
+        '''
+        :type node_ports: NodePorts
+        '''
+        if not isinstance(node_ports, NodePorts):
+            raise AssertionError(
+                'Ports definition must be of type NodePorts. Instead got: '
+                '{}'.format(type(node_ports)))
+
+        if not isinstance(node_ports.inports, Mapping):
+            raise AssertionError(
+                'Input ports must be defined as a Mapping or dict. Instead '
+                'got: {}'.format(node_ports.inports))
+
+        if not isinstance(node_ports.outports, Mapping):
+            raise AssertionError(
+                'Output ports must be defined as a Mapping or dict. Instead '
+                'got: {}'.format(node_ports.outports))
+
+        for port_name, port_spec in chain(node_ports.inports.items(),
+                                          node_ports.outports.items()):
+
+            assert isinstance(port_name, str), \
+                'Port names must be strings. Instead got: {}'.format(port_name)
+
+            if not isinstance(port_spec, Mapping):
+                raise Exception(
+                    'Port spec must be dict. Invalid port spec for port '
+                    '"{}" port spec: {}'.format(port_name, port_spec))
+
+            for port_field, field_val in port_spec.items():
+                cls._typecheck(port_field, field_val)
diff --git a/gquant/dataframe_flow/task.py b/gquant/dataframe_flow/task.py
index 3111aee7..0aacda6c 100644
--- a/gquant/dataframe_flow/task.py
+++ b/gquant/dataframe_flow/task.py
@@ -1,13 +1,13 @@
+import os
 import importlib
 import copy
-from .node import Node
 from .taskSpecSchema import TaskSpecSchema
-import os
+from ._node import _Node
+
 
 __all__ = ['Task']
 
 DEFAULT_MODULE = os.getenv('GQUANT_PLUGIN_MODULE', "gquant.plugin_nodes")
-MODLIB = importlib.import_module(DEFAULT_MODULE)
 
 
 class Task(object):
@@ -31,7 +31,7 @@ def __getitem__(self, key):
     def get(self, key, default=None):
         return self._task_spec.get(key, default)
 
-    def get_node_obj(self, replace=None):
+    def get_node_obj(self, replace=None, tgraph_mixin=False):
         """
         instantiate a node instance for this task given the replacement setup
 
@@ -45,6 +45,8 @@ def get_node_obj(self, replace=None):
         object
             Node instance
         """
+        replace = dict() if replace is None else replace
+
         task_spec = copy.copy(self._task_spec)
         task_spec.update(replace)
 
@@ -62,14 +64,40 @@ def get_node_obj(self, replace=None):
                 spec.loader.exec_module(mod)
                 NodeClass = getattr(mod, node_type)
             else:
-                global MODLIB
+                global DEFAULT_MODULE
+                plugmod = os.getenv('GQUANT_PLUGIN_MODULE', DEFAULT_MODULE)
+                # MODLIB = importlib.import_module(DEFAULT_MODULE)
+                MODLIB = importlib.import_module(plugmod)
                 NodeClass = getattr(MODLIB, node_type)
-        elif issubclass(node_type, Node):
+        elif issubclass(node_type, _Node):
             NodeClass = node_type
         else:
-            raise "Not supported"
+            raise Exception("Node type not supported: {}".format(node_type))
+
+        assert issubclass(NodeClass, _Node), \
+            'Node-type is not a subclass of "Node" class.'
+
+        if tgraph_mixin:
+            from ._node_flow import NodeTaskGraphMixin
+
+            class NodeInTaskGraph(NodeTaskGraphMixin, NodeClass):
+                def __init__(self, task):
+                    NodeClass.__init__(self, task)
+                    NodeTaskGraphMixin.__init__(self)
+
+                def __repr__(self):
+                    '''Override repr to show the name and path of the plugin
+                    node class.'''
+                    return '<{} {}.{} object at {}>'.format(
+                        self.__class__.__name__,
+                        NodeClass.__module__,
+                        NodeClass.__name__,
+                        hex(id(self)))
+
+            node = NodeInTaskGraph(task)
+        else:
+            node = NodeClass(task)
 
-        node = NodeClass(task)
         return node
 
 
diff --git a/gquant/dataframe_flow/taskGraph.py b/gquant/dataframe_flow/taskGraph.py
index b1356e9e..3dc9f313 100644
--- a/gquant/dataframe_flow/taskGraph.py
+++ b/gquant/dataframe_flow/taskGraph.py
@@ -1,7 +1,8 @@
 from collections import OrderedDict
 import networkx as nx
 import yaml
-from .node import Node, OUTPUT_ID
+from .node import Node
+from ._node_flow import OUTPUT_ID
 from .task import Task
 from .taskSpecSchema import TaskSpecSchema
 import warnings
@@ -35,39 +36,65 @@ def __init__(self, task_spec_list=None):
         '''
         :param task_spec_list: List of task-spec dicts per TaskSpecSchema.
         '''
-        self.__task_list = []
-        self.__index = 0
-        if task_spec_list is not None:
-            for task_spec in task_spec_list:
-                self.__task_list.append(Task(task_spec))
+        self.__task_list = {}
+        self.__index = None
+
+        error_msg = 'Task-id "{}" already in the task graph. Set '\
+                    'replace=True to replace existing task with extended task.'
+
+        self.__extend(task_spec_list=task_spec_list, replace=False,
+                      error_msg=error_msg)
+
+    def __extend(self, task_spec_list=None, replace=False, error_msg=None):
+        tspec_list = dict() if task_spec_list is None else task_spec_list
+
+        if error_msg is None:
+            error_msg = 'Task-id "{}" already in the task graph. Set '\
+                        'replace=True to replace existing task.'
+
+        for tspec in tspec_list:
+            task = Task(tspec)
+            task_id = task[TaskSpecSchema.task_id]
+            if task_id in self.__task_list and not replace:
+                raise Exception(error_msg.format(task_id))
+            self.__task_list[task_id] = task
 
-    def extend(self, task_spec_list=None):
+    def extend(self, task_spec_list=None, replace=False):
         '''
         Add more task-spec dicts to the graph
 
         :param task_spec_list: List of task-spec dicts per TaskSpecSchema.
         '''
-        if task_spec_list is not None:
-            for task_spec in task_spec_list:
-                self.__task_list.append(Task(task_spec))
+
+        error_msg = 'Task-id "{}" already in the task graph. Set '\
+                    'replace=True to replace existing task with extended task.'
+
+        self.__extend(task_spec_list=task_spec_list, replace=replace,
+                      error_msg=error_msg)
+
+    def __contains__(self, task_id):
+        return True if task_id in self.__task_list else False
 
     def __len__(self):
         return len(self.__task_list)
 
     def __iter__(self):
         self.__index = 0
+        self.__tlist = list(self.__task_list.values())
         return self
 
     def __next__(self):
-        if self.__index == len(self.__task_list):
+        idx = self.__index
+        if idx is None or idx == len(self.__tlist):
+            self.__index = None
             raise StopIteration
-        obj = self.__task_list[self.__index]
-        self.__index += 1
-        return obj
+        task = self.__tlist[idx]
+        self.__index = idx + 1
+        return task
 
     def __find_roots(self, node, inputs, consider_load=True):
         """
-        find the root nodes that the `node` dependes on
+        find the root nodes that the `node` depends on
 
         Arguments
         -------
@@ -86,14 +113,18 @@ def __find_roots(self, node, inputs, consider_load=True):
         if (node.visited):
             return
         node.visited = True
+
         if len(node.inputs) == 0:
             inputs.append(node)
             return
+
         if consider_load and node.load:
             inputs.append(node)
             return
-        for i in node.inputs:
-            self.__find_roots(i, inputs, consider_load)
+
+        for node_in in node.inputs:
+            inode = node_in['from_node']
+            self.__find_roots(inode, inputs, consider_load)
 
     @staticmethod
     def load_taskgraph(filename):
@@ -132,7 +163,7 @@ def save_taskgraph(self, filename):
 
         # we want -id to be first in the resulting yaml file.
         tlist_od = []  # task list ordered
-        for task in self.__task_list:
+        for task in self:
             tod = OrderedDict([(TaskSpecSchema.task_id, 'idholder'),
                                (TaskSpecSchema.node_type, 'typeholder'),
                                (TaskSpecSchema.conf, 'confholder'),
@@ -144,7 +175,7 @@ def save_taskgraph(self, filename):
         with open(filename, 'w') as fh:
             yaml.dump(tlist_od, fh, default_flow_style=False)
 
-    def viz_graph(self):
+    def viz_graph(self, show_ports=False):
         """
         Generate the visulization of the graph in the JupyterLab
 
@@ -154,9 +185,44 @@ def viz_graph(self):
         """
         G = nx.DiGraph()
         # instantiate objects
-        for o in self.__task_list:
-            for i in o[TaskSpecSchema.inputs]:
-                G.add_edge(i, o[TaskSpecSchema.task_id])
+        for itask in self:
+            task_inputs = itask[TaskSpecSchema.inputs]
+            to_task = itask[TaskSpecSchema.task_id]
+            for iport_or_tid in task_inputs:
+                # iport_or_tid: it is either to_port or task id (tid) b/c
+                #     if using ports API task_inputs is a dictionary otherwise
+                #     task_inputs is a list.
+                taskin_and_oport = task_inputs[iport_or_tid] \
+                    if isinstance(task_inputs, dict) else iport_or_tid
+                isplit = taskin_and_oport.split('.')
+                from_task = isplit[0]
+                from_port = isplit[1] if len(isplit) > 1 else None
+                if show_ports and from_port is not None:
+                    to_port = iport_or_tid
+                    common_tip = taskin_and_oport
+                    G.add_edge(from_task, common_tip, label=from_port)
+                    G.add_edge(common_tip, to_task, label=to_port)
+                    tnode = G.nodes[common_tip]
+                    tnode.update({
+                        # 'label': '',
+                        'shape': 'point'})
+                else:
+                    G.add_edge(from_task, to_task)
+
+            # draw output ports
+            if show_ports:
+                task_node = itask.get_node_obj()
+                if not task_node._using_ports():
+                    continue
+                # task_outputs = itask.get(TaskSpecSchema.outputs, [])
+                for pout in task_node._get_output_ports():
+                    out_tip = '{}.{}'.format(
+                        itask[TaskSpecSchema.task_id], pout)
+                    G.add_edge(to_task, out_tip, label=pout)
+                    tnode = G.nodes[out_tip]
+                    tnode.update({
+                        # 'label': '',
+                        'shape': 'point'})
         return G
 
     def build(self, replace=None):
@@ -180,19 +246,41 @@ def build(self, replace=None):
                     'Replace task-id {} not found in task-graph'.format(rkey),
                     RuntimeWarning)
 
-        # instantiate objects
-        task_id = TaskSpecSchema.task_id
-        for task in self.__task_list:
-            node = task.get_node_obj(replace.get(task[task_id], {}))
-            self.__node_dict[task[task_id]] = node
+        # instantiate node objects
+        for task in self:
+            task_id = task[TaskSpecSchema.task_id]
+            node = task.get_node_obj(replace.get(task_id), tgraph_mixin=True)
+            self.__node_dict[task_id] = node
 
         # build the graph
         for task_id in self.__node_dict:
             node = self.__node_dict[task_id]
-            for input_id in node._task_obj[TaskSpecSchema.inputs]:
+            task_inputs = node._task_obj[TaskSpecSchema.inputs]
+            for input_idx, input_key in enumerate(task_inputs):
+                if node._using_ports():
+                    # node_inputs should be a dict with entries:
+                    #     {iport: taskid.oport}
+                    input_task = task_inputs[input_key].split('.')
+                    dst_port = input_key
+                else:
+                    input_task = input_key.split('.')
+                    dst_port = input_idx
+
+                input_id = input_task[0]
+                src_port = input_task[1] if len(input_task) > 1 else None
+
                 input_node = self.__node_dict[input_id]
-                node.inputs.append(input_node)
-                input_node.outputs.append(node)
+                node.inputs.append({
+                    'from_node': input_node,
+                    'from_port': src_port,
+                    'to_port': dst_port
+                })
+                # input_node.outputs.append(node)
+                input_node.outputs.append({
+                    'to_node': node,
+                    'to_port': dst_port,
+                    'from_port': src_port
+                })
 
         # this part is to do static type checks
         raw_inputs = []
@@ -233,55 +321,93 @@ def run(self, outputs, replace=None):
             the results corresponding to the outputs list
         """
         replace = dict() if replace is None else replace
+
         self.build(replace)
-        output_task = Task({TaskSpecSchema.task_id: OUTPUT_ID,
-                            TaskSpecSchema.conf: {},
-                            TaskSpecSchema.node_type: "dumpy",
-                            TaskSpecSchema.inputs: []})
-        output_node = Node(output_task)
+
+        class OutputCollector(Node):
+            def columns_setup(self):
+                super().columns_setup()
+
+            def process(self, inputs):
+                return super().process(inputs)
+
+        output_task = Task({
+            TaskSpecSchema.task_id: OUTPUT_ID,
+            TaskSpecSchema.conf: {},
+            TaskSpecSchema.node_type: OutputCollector,
+            TaskSpecSchema.inputs: []
+        })
+
+        outputs_collector_node = output_task.get_node_obj(tgraph_mixin=True)
+
         # want to save the intermediate results
-        output_node.clear_input = False
+        outputs_collector_node.clear_input = False
         results = []
-        results_obj = []
-        for o in outputs:
-            o_obj = self.__node_dict[o]
-            results_obj.append(o_obj)
-            output_node.inputs.append(o_obj)
-            o_obj.outputs.append(output_node)
+        results_task_ids = []
+        for task_id in outputs:
+            nodeid_oport = task_id.split('.')
+            nodeid = nodeid_oport[0]
+            oport = nodeid_oport[1] if len(nodeid_oport) > 1 else None
+            onode = self.__node_dict[nodeid]
+            results_task_ids.append(task_id)
+            dummy_port = task_id
+            outputs_collector_node.inputs.append({
+                'from_node': onode,
+                'from_port': oport,
+                'to_port': dummy_port
+            })
+            onode.outputs.append({
+                'to_node': outputs_collector_node,
+                'to_port': dummy_port,
+                'from_port': oport
+            })
 
         inputs = []
-        self.__find_roots(output_node, inputs, consider_load=True)
+        self.__find_roots(outputs_collector_node, inputs, consider_load=True)
         # now clean up the graph, removed the node that is not used for
         # computation
         for key in self.__node_dict:
-            current_obj = self.__node_dict[key]
-            if not current_obj.visited:
-                for i in current_obj.inputs:
-                    i.outputs.remove(current_obj)
-                current_obj.inputs = []
+            node_check_visit = self.__node_dict[key]
+            if not node_check_visit.visited:
+                for inode_info in node_check_visit.inputs:
+                    inode = inode_info['from_node']
+                    oport = inode_info['from_port']
+                    iport = inode_info['to_port']
+                    onode_info = {
+                        'to_node': node_check_visit,
+                        'to_port': iport,
+                        'from_port': oport
+                    }
+                    inode.outputs.remove(onode_info)
+                node_check_visit.inputs = []
 
         for i in inputs:
             i.flow()
 
-        for r_obj in results_obj:
-            results.append(output_node.input_df[r_obj])
+        results_dfs_dict = outputs_collector_node.input_df
+        for task_id in results_task_ids:
+            results.append(results_dfs_dict[task_id])
 
         # clean the results afterwards
-        output_node.input_df = {}
+        outputs_collector_node.input_df = {}
         return tuple(results)
 
-    def draw(self, show=None, fmt='png'):
-        nx_graph = self.viz_graph()
+    def to_pydot(self, show_ports=False):
+        nx_graph = self.viz_graph(show_ports=show_ports)
         to_pydot = nx.drawing.nx_pydot.to_pydot
         pdot = to_pydot(nx_graph)
+        return pdot
+
+    def draw(self, show=None, fmt='png', show_ports=False):
+        pdot = self.to_pydot(show_ports)
         pdot_out = pdot.create(format=fmt)
 
         if show in ('ipynb',):
             from IPython.display import display
             if fmt in ('svg',):
-                from IPython.display import SVG as Image
+                from IPython.display import SVG as Image  # @UnusedImport
             else:
-                from IPython.display import Image
+                from IPython.display import Image  # @Reimport
 
             plt = Image(pdot_out)
             display(plt)
diff --git a/gquant/dataframe_flow/taskSpecSchema.py b/gquant/dataframe_flow/taskSpecSchema.py
index e05238b5..8ebe7698 100644
--- a/gquant/dataframe_flow/taskSpecSchema.py
+++ b/gquant/dataframe_flow/taskSpecSchema.py
@@ -1,5 +1,4 @@
-from .node import Node
-
+from ._node import _Node
 
 __all__ = ['TaskSpecSchema']
 
@@ -20,6 +19,7 @@ class TaskSpecSchema(object):
     conf = 'conf'
     filepath = 'filepath'
     inputs = 'inputs'
+    # outputs = 'outputs'
     load = 'load'
     save = 'save'
 
@@ -28,21 +28,26 @@ def _typecheck(cls, schema_field, value):
         if (schema_field == cls.task_id):
             assert isinstance(value, str)
         elif schema_field == cls.node_type:
-            assert (isinstance(value, str) or issubclass(value, Node))
+            assert (isinstance(value, str) or issubclass(value, _Node))
         elif schema_field == cls.conf:
             assert (isinstance(value, dict) or isinstance(value, list))
         elif schema_field == cls.filepath:
             assert isinstance(value, str)
         elif schema_field == cls.inputs:
-            assert isinstance(value, list)
+            assert (isinstance(value, list) or isinstance(value, dict))
             for item in value:
                 assert isinstance(item, str)
+        # elif schema_field == cls.outputs:
+        #     assert isinstance(value, list)
+        #     for item in value:
+        #         assert isinstance(item, str)
         elif schema_field == cls.load:
             pass
         elif schema_field == cls.save:
             assert isinstance(value, bool)
         else:
-            raise KeyError
+            raise KeyError('Uknown schema field "{}" in the task spec.'.format(
+                schema_field))
 
     _schema_req_fields = [task_id, node_type, conf, inputs]
 
diff --git a/notebooks/05b_customize_nodes_with_ports.ipynb b/notebooks/05b_customize_nodes_with_ports.ipynb
new file mode 100644
index 00000000..8a5857a9
--- /dev/null
+++ b/notebooks/05b_customize_nodes_with_ports.ipynb
@@ -0,0 +1,1586 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Customize your own GPU Kernels in gQuant\n",
+    "\n",
+    "The gQuant is designed to accelerate quantitive finance workflows on the GPU. The acceleration on GPU is facilitated by using cuDF dataframes organized into a computation graph. The cuDF project is a continously evolving library that provides a pandas-like API. Sometimes the data scientists are facing a few challenges that cannot be easily solved:\n",
+    "\n",
+    "    1. The quantitative work needs customized logic to manipulate the data, and there are no direct methods within cuDF to support this logic.\n",
+    "    2. Each cuDF dataframe method call launches the GPU kernel once. For performance crtical task, it is sometimes required to wrap lots of computation steps together in a single GPU kernel to reduce the kernel launch overheads.\n",
+    "\n",
+    "The solution is to build customized GPU kernels to implement them. The code and examples below illustrate a variety of approaches to implement customized GPU kernels in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('..')\n",
+    "\n",
+    "# Load necessary Python modules\n",
+    "import sys\n",
+    "from gquant.dataframe_flow import TaskSpecSchema, TaskGraph\n",
+    "from gquant.dataframe_flow import Node, NodePorts, PortsSpecSchema\n",
+    "import cudf\n",
+    "import numpy as np\n",
+    "from numba import cuda\n",
+    "import cupy\n",
+    "import math\n",
+    "import dask\n",
+    "import dask_cudf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define a utility function to verify the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def verify(ground_truth, computed):\n",
+    "    max_difference = (ground_truth - computed).abs().max()\n",
+    "    # print('Max Difference: {}'.format(max_difference))\n",
+    "    assert(max_difference < 1e-8)\n",
+    "    return max_difference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example Problem: Calculating the distance of points to the origin\n",
+    "\n",
+    "The sample problem is to take a list of points in 2-D space and compute their distance to the origin.\n",
+    "We start by creating a source `Node` in the graph that generates a cuDF dataframe containing some configurable number of random points. A custom node is defined by inheriting from the `Node` class and overriding methods `columns_setup` and `process`. The ports API is enabled by adding (or overriding) the `ports_setup` method. The `ports_setup` must return an instance of `NodePorts` which encapsulates the ports specs. Ports specs are dictionaries with port attributes/options per `PortsSpecSchema`.\n",
+    "\n",
+    "In the case of the `PointNode` below the input port is an empty dictionary, since no inputs are required, and the output port is called \"points_df_out\". When using ports the `process` API must return a dictionary where the keys correspond to the output ports. The `columns_setup` is as before except that the columns dictionaries must be per port."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class PointNode(Node):\n",
+    "    def ports_setup(self):\n",
+    "        input_ports = {}\n",
+    "        output_ports = {\n",
+    "            'points_df_out': {\n",
+    "                PortsSpecSchema.port_type: cudf.DataFrame\n",
+    "            }\n",
+    "        }\n",
+    "\n",
+    "        return NodePorts(inports=input_ports, outports=output_ports)\n",
+    "\n",
+    "    def columns_setup(self):\n",
+    "        self.required = {}\n",
+    "        self.addition = {\n",
+    "           'points_df_out': {\n",
+    "                'x': 'float64',\n",
+    "                'y': 'float64'\n",
+    "            }\n",
+    "        }\n",
+    "\n",
+    "    def process(self, inputs):\n",
+    "        npts = self.conf['npts']\n",
+    "\n",
+    "        df = cudf.DataFrame()\n",
+    "        df['x'] = np.random.rand(npts)\n",
+    "        df['y'] = np.random.rand(npts)\n",
+    "\n",
+    "        output = {\n",
+    "            'points_df_out': df,\n",
+    "        }\n",
+    "\n",
+    "        return output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The distance can be computed via cuDF methods. We define the `DistanceNode` to calculate the euclidean distance and add a `distance_cudf` column to the output dataframe. We will use that as the ground truth to compare and verify results later. Additionally, the distance node calculates absolute distance (Manhattan distance) in another output port which is optional.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DistanceNode(Node):\n",
+    "    def ports_setup(self):\n",
+    "        input_ports = {\n",
+    "            'points_df_in': {\n",
+    "                'type': cudf.DataFrame\n",
+    "            }\n",
+    "        }\n",
+    "\n",
+    "        output_ports = {\n",
+    "            'distance_euclid_df': {\n",
+    "                'type': cudf.DataFrame\n",
+    "            },\n",
+    "            'distance_abs_df': {\n",
+    "                PortsSpecSchema.port_type: cudf.DataFrame,\n",
+    "                PortsSpecSchema.optional: True\n",
+    "            }\n",
+    "        }\n",
+    "\n",
+    "        return NodePorts(inports=input_ports, outports=output_ports)\n",
+    "\n",
+    "    def columns_setup(self):\n",
+    "        self.delayed_process = True\n",
+    "\n",
+    "        req_cols = {\n",
+    "            'x': 'float64',\n",
+    "            'y': 'float64'\n",
+    "        }\n",
+    "\n",
+    "        self.required = {\n",
+    "            'points_df_in': req_cols,            \n",
+    "            'distance_euclid_df': req_cols,\n",
+    "            'distance_abs_df': req_cols\n",
+    "        }\n",
+    "\n",
+    "        self.addition = {\n",
+    "            'distance_euclid_df': {\n",
+    "                'distance_cudf': 'float64'\n",
+    "            },\n",
+    "            'distance_abs_df': {\n",
+    "                'distance_cudf': 'float64'\n",
+    "            }\n",
+    "        }\n",
+    "\n",
+    "    def process(self, inputs):\n",
+    "        df = inputs['points_df_in']\n",
+    "\n",
+    "        # DEBUGGING\n",
+    "        try:\n",
+    "            from dask.distributed import get_worker\n",
+    "            worker = get_worker()\n",
+    "            print('worker{} process NODE \"{}\" worker: {}'.format(\n",
+    "                worker.name, self.uid, worker))\n",
+    "            # print('worker{} NODE \"{}\" df type: {}'.format(\n",
+    "            #     worker.name, self.uid, type(df)))\n",
+    "        except (ValueError, ImportError):\n",
+    "            pass\n",
+    "        \n",
+    "        calc_absd = self.conf.get('calc_absd', False)\n",
+    "        if calc_absd:\n",
+    "            df_abs = df.copy()\n",
+    "            df_abs['distance_cudf'] = df['x'].abs() + df['y'].abs()\n",
+    "\n",
+    "        df['distance_cudf'] = (df['x']**2 + df['y']**2).sqrt()\n",
+    "        \n",
+    "\n",
+    "        output = {\n",
+    "            'distance_euclid_df': df,\n",
+    "        }\n",
+    "        \n",
+    "        if calc_absd:\n",
+    "            output['distance_abs_df'] = df_abs\n",
+    "\n",
+    "        return output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Having these two nodes, we can construct a simple task graph to compute the distance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Task specifications.\n",
+    "\n",
+    "points_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'points_task',\n",
+    "    TaskSpecSchema.node_type: PointNode,\n",
+    "    TaskSpecSchema.conf: {'npts': 1000},\n",
+    "    TaskSpecSchema.inputs: {},\n",
+    "}\n",
+    "\n",
+    "cudf_distance_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'distance_by_cudf',\n",
+    "    TaskSpecSchema.node_type: DistanceNode,\n",
+    "    TaskSpecSchema.conf: {},\n",
+    "    TaskSpecSchema.inputs: {\n",
+    "        'points_df_in': 'points_task.points_df_out'\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "task_list = [points_tspec, cudf_distance_tspec]\n",
+    "task_graph = TaskGraph(task_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can visualize the task graph with and without ports."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WITHOUT PORTS\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print('WITHOUT PORTS')\n",
+    "task_graph.draw(show='ipynb')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WITH PORTS\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print('WITH PORTS')\n",
+    "task_graph.draw(show='ipynb', show_ports=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next step is to run the task graph to obtain the distances. The output is identified by the `id` of the distance node:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ERROR: Missing output port \"distance_abs_df\" from node \"distance_by_cudf\". This output is listed in task-graph outputs.\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "task_list = [points_tspec, cudf_distance_tspec]\n",
+    "task_graph = TaskGraph(task_list)\n",
+    "\n",
+    "outlist = [\n",
+    "    'points_task.points_df_out',\n",
+    "    'distance_by_cudf.distance_euclid_df',\n",
+    "    'distance_by_cudf.distance_abs_df'\n",
+    "]\n",
+    "\n",
+    "try:\n",
+    "    (points_df, dist_euclid_df_w_cudf, dist_abs_df_w_cudf) = \\\n",
+    "        task_graph.run(outputs=outlist)\n",
+    "except Exception as err:\n",
+    "    print(err)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note the error above. We specified `distance_by_cudf.distance_abs_df` as an output, but in the `conf` of `cudf_distance_task_spec` we did not set `calc_absd` to be `True`. Therefore `distance_by_cudf.distance_abs_df` is not calculated (refer to process method of `DistanceNode` class above). Below we remove the `distance_by_cudf.distance_abs_df` from outlist and re-run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HEAD dist_euclid_df_w_cudf:\n",
+      "          x         y  distance_cudf\n",
+      "0  0.298743  0.859643       0.910073\n",
+      "1  0.241831  0.873547       0.906403\n",
+      "2  0.505219  0.054914       0.508195\n",
+      "3  0.600409  0.167747       0.623402\n",
+      "4  0.202772  0.062324       0.212134\n"
+     ]
+    }
+   ],
+   "source": [
+    "outlist = ['distance_by_cudf.distance_euclid_df']\n",
+    "(dist_euclid_df_w_cudf,) = task_graph.run(outputs=outlist)\n",
+    "print('HEAD dist_euclid_df_w_cudf:\\n{}'.format(dist_euclid_df_w_cudf.head()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Why did the above run without errors even though the `DistanceNode` defines an output port `distance_abs_df`? That's because in the `ports_setup` that port is configured to be optional.\n",
+    "```\n",
+    "'distance_abs_df': {\n",
+    "    'type': cudf.DataFrame,\n",
+    "    'optional': True\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "Note that instead of keywords `type` and `optional` we used `PortsSpecSchema` for these fields (to adhere to good programming practices). If we were to set `output_ports` in the `DistanceNode` as below:\n",
+    "```\n",
+    "output_ports = {\n",
+    "    'distance_euclid_df': {\n",
+    "        'type': cudf.DataFrame\n",
+    "    },\n",
+    "    'distance_abs_df': {\n",
+    "        'type': cudf.DataFrame\n",
+    "    }\n",
+    "```\n",
+    "Then the `distance_abs_df` would be non-optional and above would have produced an error as well. Try it out yourself by editing the `DistanceNode` and re-running the task-graph (remember to re-instantiate the `cudf_distance_task_spec`).\n",
+    "\n",
+    "Below we set the `conf` to calculate absolute distance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "replace_spec = {\n",
+    "    'distance_by_cudf': {\n",
+    "        TaskSpecSchema.conf: {\n",
+    "            'calc_absd': True\n",
+    "        }\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "outlist = [\n",
+    "    'points_task.points_df_out',\n",
+    "    'distance_by_cudf.distance_euclid_df',\n",
+    "    'distance_by_cudf.distance_abs_df'\n",
+    "]\n",
+    "(points_df, dist_euclid_df_w_cudf, dist_abs_df_w_cudf) = \\\n",
+    "    task_graph.run(outputs=outlist, replace=replace_spec)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could have setup the `cudf_distance_tspec` to calculate absolute distance to begin with and obtained all the outputs without errors. The above was meant to demonstrate how to work with ports."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "points_df:\n",
+      "          x         y\n",
+      "0  0.887968  0.582714\n",
+      "1  0.146722  0.296758\n",
+      "2  0.391815  0.623228\n",
+      "3  0.882974  0.621067\n",
+      "4  0.794594  0.844349\n",
+      "\n",
+      "dist_euclid_df_w_cudf:\n",
+      "          x         y  distance_cudf\n",
+      "0  0.887968  0.582714       1.062094\n",
+      "1  0.146722  0.296758       0.331048\n",
+      "2  0.391815  0.623228       0.736161\n",
+      "3  0.882974  0.621067       1.079522\n",
+      "4  0.794594  0.844349       1.159442\n",
+      "\n",
+      "dist_abs_df_w_cudf:\n",
+      "          x         y  distance_cudf\n",
+      "0  0.887968  0.582714       1.470682\n",
+      "1  0.146722  0.296758       0.443480\n",
+      "2  0.391815  0.623228       1.015043\n",
+      "3  0.882974  0.621067       1.504041\n",
+      "4  0.794594  0.844349       1.638943\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('points_df:\\n{}\\n'.format(points_df.head()))\n",
+    "print('dist_euclid_df_w_cudf:\\n{}\\n'.format(dist_euclid_df_w_cudf.head()))\n",
+    "print('dist_abs_df_w_cudf:\\n{}\\n'.format(dist_abs_df_w_cudf.head()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Customized Kernel with Numba library\n",
+    "\n",
+    "Numba is an excellent python library used for accelerating numerical computations. Numba supports CUDA GPU programming by directly compiling a restricted subset of Python code into CUDA kernels and device functions. The Numba GPU kernel is written in Python and translated (JIT just-in-time compiled) into GPU code at runtime. This is achieved by decorating a Python function with `@cuda.jit`. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just like a C/C++ CUDA GPU kernel, the `distance_kernel` function is called by thousands of threads in the GPU. The thread id is computed by `threadIdx.x`, `blockId.x` and `blockDim.x` built-in variables. Please check the [CUDA programming guild](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#thread-hierarchy) for details."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A cuDF series can be converted to GPU arrays compatible with the Numba library via `to_gpu_array` API. The next step is to define a Node that calls this Numba kernel to compute the distance and save the result into `distance_numba` column in the output dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@cuda.jit\n",
+    "def distance_kernel(x, y, distance, array_len):\n",
+    "    # ii - overall thread index\n",
+    "    ii = cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x\n",
+    "    if ii < array_len:\n",
+    "        distance[ii] = math.sqrt(x[ii]**2 + y[ii]**2)\n",
+    "\n",
+    "\n",
+    "class NumbaDistanceNode(Node):\n",
+    "\n",
+    "    def ports_setup(self):\n",
+    "        input_ports = {\n",
+    "            'points_df_in': {\n",
+    "                PortsSpecSchema.port_type: cudf.DataFrame\n",
+    "            }\n",
+    "        }\n",
+    "\n",
+    "        output_ports = {\n",
+    "            'distance_df': {\n",
+    "                PortsSpecSchema.port_type: cudf.DataFrame\n",
+    "            }\n",
+    "        }        \n",
+    "\n",
+    "        return NodePorts(inports=input_ports, outports=output_ports)\n",
+    "    \n",
+    "    def columns_setup(self,):\n",
+    "        self.delayed_process = True\n",
+    "\n",
+    "        required = {'x': 'float64',\n",
+    "                    'y': 'float64'}\n",
+    "        self.required = {\n",
+    "            'points_df_in': required,\n",
+    "            'distance_df': required\n",
+    "        }\n",
+    "        self.addition = {\n",
+    "            'distance_df': {'distance_numba': 'float64'}\n",
+    "        }\n",
+    "\n",
+    "    def process(self, inputs):\n",
+    "        df = inputs['points_df_in']\n",
+    "\n",
+    "        # DEBUGGING\n",
+    "        try:\n",
+    "            from dask.distributed import get_worker\n",
+    "            worker = get_worker()\n",
+    "            print('worker{} process NODE \"{}\" worker: {}'.format(\n",
+    "                worker.name, self.uid, worker))\n",
+    "            # print('worker{} NODE \"{}\" df type: {}'.format(\n",
+    "            #     worker.name, self.uid, type(df)))\n",
+    "        except (ValueError, ImportError):\n",
+    "            pass\n",
+    "\n",
+    "        number_of_threads = 16\n",
+    "        number_of_blocks = ((len(df) - 1)//number_of_threads) + 1\n",
+    "        # Inits device array by setting 0 for each index.\n",
+    "        # df['distance_numba'] = 0.0\n",
+    "        darr = cuda.device_array(len(df))\n",
+    "        distance_kernel[(number_of_blocks,), (number_of_threads,)](\n",
+    "            df['x'].to_gpu_array(),\n",
+    "            df['y'].to_gpu_array(),\n",
+    "            darr,\n",
+    "            len(df))\n",
+    "        df['distance_numba'] = darr\n",
+    "        return {'distance_df': df}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `self.delayed_process = True` flag in the `columns_setup` is necesary to enable the logic in the `Node` class for handling `dask_cudf` dataframes in order to use Dask (for distributed computation i.e. multi-gpu in examples later on). The `dask_cudf` dataframe does not support GPU customized kernels directly. The `to_delayed` and `from_delayed` low level interfaces of `dask_cudf` enable this support. The gQuant framework handles `dask_cudf` dataframes automatically under the hood when we set this flag."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Customized Kernel by CuPy library\n",
+    "\n",
+    "CuPy is an alternative to Numba. Numba JIT compiles Python code into GPU device code at runtime. There are some limitations in how Numba can be used as well as JIT compilation latency overhead. When a Python process calls a Numba GPU kernel for the first time Numba has to compile the Python code, and each time a new Python process is started the GPU kernel has to be recompiled. If advanced features of CUDA are needed and latency is important, CuPy is an alternative library that can be used to compile C/C++ CUDA code. CuPy caches the GPU device code on disk (default location `$(HOME)/.cupy/kernel_cache` which can be changed via `CUPY_CACHE_DIR` environment variable) thus eliminating compilation latency for subsequent Python processes.\n",
+    "\n",
+    "`CuPy` GPU kernel is esentially a C/C++ GPU kernel. Below we define the `compute_distance` kernel using `CuPy`:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using gQuant we can now define a Node that calls this CuPy kernel to compute the distance and save the results into `distance_cupy` column of a `cudf` dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_kernel = cupy.RawKernel(r'''\n",
+    "    extern \"C\" __global__\n",
+    "    void compute_distance(const double* x, const double* y,\n",
+    "            double* distance, int arr_len) {\n",
+    "        int tid = blockDim.x * blockIdx.x + threadIdx.x;\n",
+    "        if (tid < arr_len){\n",
+    "        distance[tid] = sqrt(x[tid]*x[tid] + y[tid]*y[tid]);\n",
+    "        }\n",
+    "    }\n",
+    "''', 'compute_distance')\n",
+    "\n",
+    "\n",
+    "class CupyDistanceNode(Node):\n",
+    "\n",
+    "    def ports_setup(self):\n",
+    "        input_ports = {\n",
+    "            'points_df_in': {\n",
+    "                PortsSpecSchema.port_type: cudf.DataFrame\n",
+    "            }\n",
+    "        }\n",
+    "\n",
+    "        output_ports = {\n",
+    "            'distance_df': {\n",
+    "                PortsSpecSchema.port_type: cudf.DataFrame\n",
+    "            }\n",
+    "        }\n",
+    "\n",
+    "        return NodePorts(inports=input_ports, outports=output_ports)\n",
+    "\n",
+    "    def columns_setup(self,):\n",
+    "        cols_required = {'x': 'float64',\n",
+    "                         'y': 'float64'}\n",
+    "        self.required = {\n",
+    "            'points_df_in': cols_required,\n",
+    "            'distance_df': cols_required            \n",
+    "        }\n",
+    "\n",
+    "        self.addition = {\n",
+    "            'distance_df': {\n",
+    "                'distance_cupy': 'float64'\n",
+    "            }\n",
+    "        }\n",
+    "        self.delayed_process = True\n",
+    "\n",
+    "    def process(self, inputs):\n",
+    "        df = inputs['points_df_in']\n",
+    "        # cupy_x = cupy.asarray(df['x'].to_gpu_array())\n",
+    "        # cupy_y = cupy.asarray(df['y'].to_gpu_array())\n",
+    "        cupy_x = cupy.asarray(df['x'])\n",
+    "        cupy_y = cupy.asarray(df['y'])\n",
+    "        number_of_threads = 16\n",
+    "        number_of_blocks = (len(df) - 1)//number_of_threads + 1\n",
+    "        dis = cupy.ndarray(len(df), dtype=cupy.float64)\n",
+    "        raw_kernel((number_of_blocks,), (number_of_threads,),\n",
+    "                   (cupy_x, cupy_y, dis, len(df)))\n",
+    "        df['distance_cupy'] = dis\n",
+    "\n",
+    "        return {'distance_df': df}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `self.delayed_process = True` flag is added for the same reason as with `DistanceNumbaNode` i.e. to support `dask_cudf` data frames."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Computing using the Nodes with customized GPU kernels\n",
+    "\n",
+    "First we construct the computation graph for gQuant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7O3/00UdhYWF43YEKgPEEEgUARjuhUJiUlHT69On4+HihUDhnzpygoKBly5bBgTJCqKSk5MaNG3FxcdnZ2RoaGqtXr/7oo4+mT5+Od1wAjB+QKAAwZrS3t//111/Xr1//+++/eTyeq6urr6+vj48PlUqdUMP4Ozo6kpOTExMT79+/X1xcrKurGxAQEBQU5O3tLSsri3d0AIw3kCgAMPbweLx79+799ddfiYmJlZWVSkpKs2fP9vb2dnNzc3R0VFBQwDtAyWOxWFlZWenp6YmJiZmZmQKBwN7e3sfHx9/f38PDg0gk4h0gAOMWJAoAjG2vXr1KTExMSkp69OhRY2MjmUy2t7d3dXV1dXWdMWMGhUIZo50NHA6nqKgo61+lpaUikcjc3Nzb29vHx8fb2xuuYgBAOiBRAGD8ePXqVWZmZlZWVmZmZk5OTldXF5lMplAoNjY2dnZ21tbWVlZW5ubm8vLyeEf6ura2toqKiuLi4oKCgqKiooKCgsrKSqFQqKamNnPmTFdXVxcXF1dXVz09PbwjBWDCgUQBgPGpu7u7qKgI2+kWFhYWFhZWVFRg/+8GBgbm/zIxMdHX19fV1TU0NNTR0RnRHILD4dDpdAaDwWQy6+rqqqqqKisrKyoqKioqWlpaEEJkMnnq1Kk2Nja2trZYcjN58mQZGbgvHAB4gkQBgImCzWa/evWq4n9VV1d3dHSI51FXV9fX11dTU1NRUdHQ0FBRUVFWVlZRUVFRUUEIaWhoYLORyWTxlYdtbW3YPaf5fD5WFIvF6uiltbWVxWLR6XQulyuuSFtb28TExPx/WVhYwGhEAEYbSBQAmOh4PB6DwaDT6Y2NjdgRf3t7O7aD7+jowF5zOByBQNDe3o4t0tnZyePxsNcqKiokEgkhRCAQ1NXVEUJYnoFRVVVVV1dXV1c3MDDQ0dHR19fX19fX0dGBhACAsQISBQDAUCQkJPj5+bW0tIi7GQAA4xKc/AMAAABAnyBRAAAAAECfIFEAAAAAQJ8gUQAAAABAnyBRAAAAAECfIFEAAAAAQJ8gUQAAAABAnyBRAAAAAECfIFEAAAAAQJ8gUQAAAABAnyBRAAAAAECfIFEAAAAAQJ8gUQAAAABAnyBRAAAAAECfIFEAAAAAQJ8gUQAAAABAnyBRAAAAAECfIFEAAAAAQJ8gUQAAAABAnyBRAAAAAECfIFEAAAAAQJ8gUQAAAABAnyBRAAAAAECfSHgHAAAA0tPR0aGioiJ+y2QyExMTGxsbfXx8bGxscAwMgFELehQAANL24MEDKpVaWVkpzUpPnTo1e/bsadOmiaeUlpauW7fO09Pz4cOHjo6O7e3t0owHgLECEgUAgLS1trbW1NRwOJyBzEyn0yVS6bp164RCoUAgEE/ZuXOnra2tsbHx2bNn//jjD1VVVYlU1JukggcAR5AoAACkLTg4uK6ubiBd/SwWa9WqVRKplEgkGhkZ9Z5y7949NTU1hJCampqkaulNgsEDgCNIFAAAoxSHw1m+fPkInaHg8XgD7NIYmhENHgBpgkQBADB0e/futba2rqurCwgI0NTUdHFxycjIEH967dq1jRs3btmyZeHChbt27erq6sKmNzY2Hjt2LDMzEyGUm5u7detWCwsLDoezbt06bW1tFxeX8vJyhNCNGzdKSkqampoiIiIOHDiAEMrLy1uzZk1MTMzmzZvXr18/kAhv3rwZGRm5bdu2TZs2iU8EnD17NjIyEiEUGxsbERGxf//+d5bz1u9y5coVFRUVY2NjhFBbW9uePXuIRKKbm9tbgwdgrBIBAMDg3b9/HyFEoVCIROLnn3/+8OHDuLg4LS0tRUXF+vp6kUh08OBBd3f37u5ukUjU1NRkaWnp5eUlFAppNJqnpydC6Nq1ayKRiE6n+/j4IIQ2bNhQWFiYk5MjJye3YsUKrJYlS5aYmZmJK6VQKDQaTSQScblcT0/PdwZ58eJFKpXK4/GwGHR0dPT19bGPmpqaEEJ79+4dyJft67uIRCI/Pz8jIyPxnHZ2dlQq9a3BAzBGQY8CAGDonJ2dCQRCTEzMnDlzgoKCjh8/zuVyT548yWQyv/76608++YRMJiOEtLS0du7cmZKScuHCBXd396+//lpcgr6+/syZMxFC3333nbW19fTp02fOnJmdnf1mXT09PaWlpTk5OQghBQWFTz/9tP/YuFzuli1bNm3aJC8vj8WAJSiD1c93QQgpKir2nllJSWkIVQAwmkGiAAAYOhkZGRKJhO1BEUKBgYGysrIFBQUZGRkcDgfrk8csWbIEIfTo0SP0xs6VSCQihEik/7uti5GRUUdHx5t1kclkPz+/qKio9evXs1islStX9h9bamoqnU63s7MTT5GVlR38V0T9fxcAxj1IFAAAEkMmkw0NDfl8flVVFUKopaVF/JG2tjZ2VmI45V+/fj00NPTEiRMUCiUlJaX/mUtKSrCQhlMjQmiEvgsAYwUkCgAASeru7qZQKObm5gghbExibxQKZTiFk8nkS5cunT9/HiHk5+eHpQJ9wfoPsN38cIzQdwFgrIBEAQAgMUwms6GhITg4mEqlqqioxMfHiz+qq6vjcrlLly4dVIEyMjJsNht73dXVdeLECYRQWFhYRkaGUCh88OBBP8va29sjhGJjY8VTet9wSSQSDTCG/r8LiURis9niYtlstlAofDN4AMYuSBQAAMPS1dVVUFCAvf7hhx/CwsKoVKq2tnZ0dHRaWlpSUhL20ZEjR8LDw729vRFCDAYDIYRdd4AQamtrQwjx+XzsLYPBwK5TQAgZGho2NTVlZ2cnJydzudzTp09ju2QjIyM1NTVHR8d+AnN3d589e/aZM2dOnjzJ5XKfPHlCo9EaGxsvXbrE5XKrq6sRQlwu951fsP/vYmdnx2KxoqOjy8rK9u7d29XVVVZW9uzZszeDH9xqBWDUIH777bd4xwAAGHvKy8vPnz/v4OBQVFTE5XIvXLiQkJCgr69/8OBBAoGAEHJxcXFwcDh8+HBWVlZmZqampmZMTAyBQHj06FFMTExlZSWTyTQ3N6+qqjpw4ACLxWKz2S4uLvHx8adOnero6CAQCJ6enqampnfu3Ll16xaVSrW1tT1//vzt27fr6uouXLgQHh4eEBDQf5CBgYENDQ2//vrryZMnlZWVDQwM7O3t3dzc2Gz2wYMHCwoKamtrdXR0TExMsCsj+tLXd0EIOTo6FhYWXrhwIT09fdOmTUwmk0KhmJqaWlpampiYiIN3cHCQ4MoHQJoIA+9/AwAAsYSEBD8/v/Dw8NjYWB6Ph3c4AICRAo+ZBgCMYUZGRuIbPr7m3LlzCxculHI5AIw/kCgAAIaOxWJ1d3ez2WxlZWVcAqitrR1V5QAw/sBgRgDA0CUlJQmFwk2bNtFoNLxjAQCMCOhRAAAMXW1trYaGBt5RAABGEPQoAAAAAKBPkCgAAAAAoE+QKAAAAACgT5AoAAAAAKBPkCgAAAAAoE+QKAAAAACgT5AoAAAAAKBPkCgAAAAAoE+QKAAAAACgT3BnRgDAgHR3d/d+IAKdTkcIVVZWtra2YlMIBIK5uTk+wQEARgw8ZhoAMCA8Hk9PT6+jo6OvGahU6uPHj6UZEgBACuDUAwBgQBQUFJYtW0YkEt/6qYyMzMqVK6UcEgBACiBRAAAM1KpVqwQCQV+fLl++XJrBAACkAxIFAMBA+fj4aGpqvjmdSCTOmTNHX19f+iEBAEYaJAoAgIEikUgrV64kk8lvfhQeHi79eAAAUgCDGQEAg5CWlubh4fHaRDKZzGQy1dXVcQkJADCioEcBADAIs2bNMjQ07D2FRCItWrQIsgQAxitIFAAAg0AgEN5///3eZx+EQmFYWBiOIQEARhScegAADE5+fr6Dg4P4raKiYlNTk4KCAo4hAQBGDvQoAAAGx97enkKhYK/JZPJ7770HWQIA4xgkCgCAQQsPD8fOPvT09KxatQrvcAAAIwhOPQAABu3Vq1eWlpYikUhDQ4PJZJJI8NQYAMYt6FEAAAza5MmTHR0dEUKrV6+GLAGA8Q0SBQDAUPj5+SGEvL298Q4EADCyIFEAAAyOUCgMDQ3dt28fQigkJAR7AQAYr2CMAgBgcM6cORMRESF+OhSBQMjNzbW3t8c3KgDACIEeBQDAQPH5/Nzc3EuXLhEIBPFEEol05MiRV69e4RgYAGDkQI8CAKBPQqGwpKTk6dOnT548efr0aW5ubmdnp4KCgkAg6O7uxuYhEAgyMjICgUBTU3Pmv5ydnV+70zMAYIyCRAEA8D/q6+uz/5Went7S0kImky0tLZ3+paOjM2PGjO7ubj6fTyaTzc3Nnzx5UlNTk5aWRqPRsrOzS0pKhEKhgYGBeBEqlaqjo4P3NwMADAUkCgBMdI2NjY8fP87IyHj69OnTp09bW1tJJJKtra2zs7Ozs/PMmTPt7Oxee7R0cXHxzp07s7KygoODd+3apaur2/vTtrY2rBMCU1NTQyAQpkyZgnU2uLm5zZgx463PqgYAjEKQKAAw4QgEgufPn6enp2P5wYsXLwgEgpWVFXbKwNnZefr06e+8K3NCQoKfn19LS4uGhkb/czY0NGBnLp48eZKVldXc3CwvL+/s7Ozm5jZr1iw3Nzc9PT3JfTkAgIRBogDAhNDc3JyRkZGRkZGenp6VlcVms1VVVV1dXd3c3KhUKpVKfef+/jUDTxReU19fj52kSEtLy8nJwU5SeHh4uLu7Ozk5ubi4yMrKDqpAAMCIgkQBgHGLTqfTaLS37pI9PDwcHR1lZIZ+3dOQE4Xe2Gx2bm4uljdg4yGUlJTc3NywCD08POTl5YdcOABAIiBRAGBcKS0tpdFoKSkpqampFRUVZDLZ2dnZ09PT09PTzc1NS0tLUhVJJFHoTSgUFhYW0mi01NTUlJSUuro6eXl5FxeX2bNnY8ErKytLpCIAwKBAogDA2CYUCouLi7GD8uTk5OrqakVFRUdHR6znwMvLS01NbSTqlXii8BrsDEViYiKNRisuLpaRkZk+fTrW0+Dt7S3BjAcA0D9IFAAYk8rLyxMTExMTE5OSklpaWpSVlalUKrYf9fT0lJOTG+kARjpR6K2+vh7rI0lJSSksLCQQCPb29vPmzfP29vby8oKeBgBGFCQKAIwZ1dXVSUlJDx48SEpKotPpqqqqs2fP9vb2njt3rp2d3XAGHAyBNBOF3pqbm2k0GrYSCgsLyWSyq6urt7f3vHnzqFQqDIQEQOIgUQBgVGtqanr48CE2IDE7O5tEIjk4OPj4+Pj4+Hh5eeG4X8QrUeiNyWQmJycnJiYmJCRUVFQoKirOmjXLx8fH3d2dSqXC868BkAhIFAAYdXg8XnJy8v3795OSkgoKCohEorOzs7e3t7e3t7u7+yi5EGA0JAq9vXr1CutuefjwIZPJ1NTUnDNnzrx58+bPnz958mS8owNgDINEAYDR4tWrV3///ffdu3cfPXrE4/Hs7OywHvXZs2erqqriHd3rRluiICYSiQoKCrCkITk5uaOjY+rUqQsXLly4cOHs2bNHSZoFwBgCiQIAeOrs7KTRaNiwxOzsbOwuAkuWLAkKCjI2NsY7uv6M2kShN4FAkJube/v27Tt37jx79kxeXt7d3d3Hx2fp0qXTpk3DOzoAxgZIFADAQUVFRUJCQmJi4r1799rb2y0sLJYsWeLv74/vsINBGROJQm8MBuPevXt37txJSEhgsVgWFhbYUI+FCxfCdRMA9AMSBQCkpKenJzk5GTu5UFJSoqKigu2lFi5caGRkhHd0gzbmEgWxnp6etLS0v//++59//snPz1dUVJw7d+6iRYsWLVpkZmaGd3QAjDqQKAAwsjo7OxMSEu7cuRMfH89kMsWdB9K528HIGbuJQm9MJvOff/4RdzNYW1uHhIT4+/s7OTnhHRoAowUkCgCMCC6Xm5SUFBsbGx8fz+FwHB0dlyxZsmrVqqlTp+IdmmSMj0RBrKen59GjRzdu3Lh582Z9fb2lpWVgYGBgYKCLi4uUb1ABwGgDiQIAktTc3PzXXz3T5o8AACAASURBVH/FxsYmJCTw+XwqlRoSEhISEmJoaIh3aBI2zhKF3goLC2NjY69cuVJSUqKjo7NgwYKQkJD58+ePleEjAEgWJAoASEB1dfWNGzfu3Lnz6NEjEonk4+Pj7++/bNkyXV1dvEMbKeM4URArLCy8c+fO7du309PT1dXVfXx8lixZEhgYqKKigndoAEgPJAoADF1VVdXly5djY2OfPXumrq6+ePHioKCgBQsWKCoq4h3aiJsIiYJYRUXFjRs3bty4kZ6eLi8vP3/+/OXLl/v7+yspKeEdGgAjDhIFAAatsbHx6tWrly9fTk9P19TUDA4ODgoKmjt37oTqmp5QiYIYg8G4efPm9evXk5KS5OTkli5dunLlSjgrAcY3SBQAGKiOjo74+PjLly8nJCTIy8sHBASsXLnSz8+PTCbjHRoOJmaiINbY2BgbG4sli2pqakFBQStXrpw7dy6MfATjDyQKALxbdnb2L7/8cunSJR6PN3fu3PDw8KCgoAl+l54JniiI1dbWxsXFxcbGpqWlGRoavvfeex9++KGDgwPecQEgMZAoANCnysrKs2fPnj17tqKiYubMmWvWrFmxYoWmpibecY0KkCi8pqSk5Ny5cxcuXKipqaFSqe+///6KFStg5YBxABIFAF7X1dV17dq106dPJycn6+johIWFrV271sbGBu+4RhdIFN5KKBQ+ePDg7Nmz169fFwqFS5cu/fDDD319feGUBBi7oO0C8P978eLF1q1bjYyM1q5dq66uHh8fX1tbe+DAAcgSwADJyMj4+PicP3++oaHh+PHjdDp9wYIFU6ZM+fHHH+l0Ot7RATAU0KMAABIIBHfv3j1y5EhSUpKBgUF4ePj69etNTEzwjmvQOjo6el/iz2QyExMTGxsbfXx8JJ7rjNEeBWmuIkxZWdnvv//++++/t7S0zJ07NzIyMigoiEgkjkRd44z0fyzwVtCjACa0urq6/fv3m5ubL1u2DCF05cqVqqqqffv2DSdLePDgAZVKrayslFiUA3Dq1KnZs2f3fnRyaWnpunXrPD09Hz586Ojo2N7e3tey0g94Qq2iqVOn7tu3r6am5vLlywih0NBQU1PT7du3V1dXD6E06ZtQPxZ4K0gUwEQkFApv3bq1ePFiExOTI0eOrFmzBnvuc0hICIlEGmbhra2tNTU1HA5nIDNLqjt63bp1QqFQIBCIp+zcudPW1tbY2Pjs2bN//PGHqqpqX8sOKmCJmICrSE5OLiQkJCEhoaioaPny5b/99tuUKVOCg4Pv3bsnFAqHXKwUTMAfC7xOBMBE0tbWdurUKSsrK+xc8tWrV7u7u/EKprW1dc6cOZIqbcWKFfr6+uK3SkpK+/btk1Thb7p//z5CqKWlZeSqEI3xVdSPzs7Oq1ev+vj4EAiEyZMn79u3b6TXpBSM1x8LQI8CmChevny5fft2U1PTLVu2eHl5FRQUYF0IeN0uicPhLF++fIQ6SHk83jg4ohrHq0jcwVBQUODr67tnzx5TU9ONGzeWlpbiFdIwjeMfC0CiAMY/Go22fPlyKyur2NjY7du3V1VVnTp1ytrauv+lioqK/vOf/1hbW9fV1QUEBGhqarq4uGRkZIhnuHbt2saNG7ds2bJw4cJdu3Z1dXVh0xsbG48dO5aZmYkQys3N3bp1q4WFBYfDWbdunba2touLS3l5OULoxo0bJSUlTU1NERERBw4cQAjl5eWtWbMmJiZm8+bN69evH8hXu3nzZmRk5LZt2zZt2iTu9T179mxkZCRCKDY2NiIiYv/+/f0XMsCA32rv3r2wivpfRf2zsbE5ceJEfX39gQMHkpKSrKysPDw8YmNje/e6SwS05+H/WBMX3l0aAIwUHo939uxZbHS0u7v71atX+Xz+wBdPSUmxtrYmEomff/75w4cP4+LitLS0FBUV6+vrRSLRwYMH3d3dsdMWTU1NlpaWXl5eQqGQRqN5enoihK5duyYSieh0uo+PD0Jow4YNhYWFOTk5cnJyK1aswKpYsmSJmZmZuEYKhUKj0UQiEZfL9fT0fGeEFy9epFKpPB4Pi0FHR0fcVdvU1IQQ2rt37zsLGVTAvWGnHigUCqyivlbRYAkEgoSEhCVLlhAIBAsLi3379jU3Nw+/WAy0Z8n+WBMKJApgHKqtrd26dau6urqCgsK6devy8/OHVs7atWtJJJJ4EMOVK1cQQrt372YwGEpKSufPnxfPeebMGYTQuXPnRP/uQbHtlEgk2rFjB0KoqakJe+vh4WFpaYm97r1h7e7uRggdPXoUe3vp0qX+Y+NwOAYGBr1nCwoKGsKGdVABv7nU6tWrYRX1tYqGrLi4eMOGDSoqKsrKyp9++mlZWZlEioX2LBqBH2sigFMPYFx5/vz5mjVrLCwsLly48NVXX9XU1Pz66692dnZDK41IJJJIJPEghsDAQFlZ2YKCgoyMDA6HY2xsLJ5zyZIlCKFHjx4hhF57xjR2xbz4YgojI6OOjo436yKTyX5+flFRUevXr2exWCtXruw/ttTUVDqd3vurDfkBhkMLGCMjIwOrqP9VNARWVlbHjh2rra3du3fv/fv3raysli1blpqaOsxioT33EzDoByQKYJyg0Wj+/v729vZpaWkxMTHl5eU7duzQ0tKSYBVkMtnQ0JDP51dVVSGEWlpaxB9pa2tjvbjDKf/69euhoaEnTpygUCgpKSn9z1xSUoKFNJwaJQ5WkQSpqqpGRUWVlZXFx8dzOBwvL68ZM2acO3eOz+dLpHz4scAAQaIAxjahUHj79m03NzdPT8/W1tYrV66UlpZGRUXJy8uPRHXd3d0UCsXc3Bwh9OaQKAqFMpzCyWTypUuXzp8/jxDy8/PDNp19wY63sE38qAKrSLJkZGT8/f0TEhKePn1qa2v70UcfWVpa7t+/n8ViDb9w+LHAQECiAMaqrq6uc+fOTZs2bdmyZdra2unp6TQaLSQkZOSevsNkMhsaGoKDg6lUqoqKSnx8vPijuro6Lpe7dOnSQRUoIyPDZrOx111dXSdOnEAIhYWFZWRkYM8W6mdZe3t7hFBsbKx4Su8b1IhwujU7rKKR4+TkdO7cubKystDQ0OjoaBMTk6ioqOHc3hF+LDBAkCiAsaelpeW7776bNGnSp59+6uvrW1ZWhnUqjERdXV1dBQUF2OsffvghLCyMSqVqa2tHR0enpaUlJSVhHx05ciQ8PNzb2xshxGAwEELY8CuEUFtbG0JI3F3MYDCwcd0IIUNDw6ampuzs7OTkZC6Xe/r0aWzLaGRkpKam5ujo2E9g7u7us2fPPnPmzMmTJ7lc7pMnT2g0WmNj46VLl7hcLrb/4HK5A/mOAw8YVtHQVpEEmZub79u3r6KiYufOndeuXZsyZcr777///PnzAS4OP5Y0f6xxg/jtt9/iHQMAA9XQ0LBnz56wsLCnT59u3Ljx8uXL7733nqam5ghVd/v27YKCAi6Xe+HChYSEBH19/YMHDxIIBISQi4uLg4PD4cOHs7KyMjMzNTU1Y2JiCATCo0ePYmJiKisrmUymubl5VVXVgQMHWCwWm812cXGJj48/depUR0cHgUDw9PQ0NTW9c+fOrVu3qFSqra3t+fPnb9++XVdXd+HChfDw8ICAgP7DCwwMbGho+PXXX0+ePKmsrGxgYGBvb+/m5sZmsw8ePFhQUFBbW6ujo2NiYtLPiZhBBdy7t6a8vPz8+fMODg5FRUWwit66ikaOgoKCh4fHxo0bzc3N4+Livv/+++zsbFNT094DEt8E7RmXH2s8wO16CwAGo6qqatOmTYqKirq6ut988w2LxZJCpevWrZOXl5dCRWMRdhFaeHg4rCJ8YQ8umTVrFkLI3d391q1bQqHwrXNCewZDM9zn3wAw0srLyw8fPnzq1Cl9ff0ff/wxMjJSQUEB76CkxMjISHyDvNecO3du4cKFUi5nFIJVRCAQ/P39/f39aTTa/v37AwIC7Ozsvvzyy1WrVg3/CWeSBT/WGDW6mhEAvRUUFPz3v/+9dOmSqanp/v37P/nkEzk5OWkG0NLS0t3dzWazlZWVpVmvWG1t7agq500sFgtW0Sjh4eHh4eGRn59/4MCBjz766Ntvv42KiuqdWEN7BkMDZ2jAaPT06dOlS5c6ODgUFBRcunTpxYsXUVFRUs4SduzYgT0CeNOmTTQaTZpVjyFJSUmwikYVe3v7c+fOFRUV+fj4bNu2bfLkyT/99BOHw4H2DIaMIILBn2A0ycrK+v777//66y9XV9evv/560aJF2GArMNokJCT4+fm1tLRoaGjgHQt4u/r6+oMHD546dUpBQWHz5s0bNmxQVVXFOygw9kCPAhgtcnNzly9fTqVSm5ubb9269fjx48WLF0OWAMCQGRoaHjhwoLKycv369TExMcbGxtu3b+99B0YABgISBYC/jIwMf3//GTNm1NbW3rx58/Hjx/7+/pAiACARWlpa3377bXV19c6dO3/99VdTU9OoqKiGhga84wJjBiQKAE9YTuDm5tbS0nLz5s309HR/f3+8gwJgHFJRUdm2bVtVVdXevXuvXr06ZcqUqKioYT7NAUwQkCgAfGRkZPj5+c2aNYvFYiUkJKSlpUGKAMBIU1ZWjoqKevHixbfffnvlyhVLS8vPP/+cTqfjHRcY1SBRANKWk5OD9SLweLykpKTU1FQfHx+8gwJgAlFWVt6yZUtFRUV0dPS1a9cmT578xRdfYHc+BuBNkCgA6SkuLn7//fednZ2rq6uvXr2ampqK3UweACB9CgoKmzZtKi8vP3To0NWrV01NTT/++GPoXQBvgkQBSENlZeXHH39sZ2f37NmzP//8Mzc3NyQkBO+gAABIVlY2MjKyvLz8yJEjd+7csbS0hKGO4DWQKICRVVNT8/HHH1taWqakpPz+++95eXkhISFwRQMAo4o4XTh48GBsbCw21BFORgAMJApgpDQ0NHz22WdTpkxJTEw8ffr08+fP33//fSKRiHdcAIC3k5OTi4yMfPHixe7duy9dumRpablr167W1la84wI4g0QBSF57e/vu3bunTJly8+bNo0ePlpSUQIoAwFihpKT01VdfVVRU7Nix4+TJk5MnT46OjuZwOHjHBXADt3AGktTT03PmzJndu3d3d3dv27Zt06ZNE+dJj+Mei8WaOnUqj8fD3opEIj6fTyaTxTNMmzYtKysLp+jAiGCz2T///HN0dDSZTN6yZUtUVJS8vDzeQQFpgx4FIBlCoTA2NnbatGkbN24MCAgoLS3dtm0bZAnjibq6+owZM7hcLpvNZrPZHA6nq6uL/S8Oh7NgwQK8YwQSpqysvG3btlevXn300UffffcdhUL55ZdfBAIB3nEBqYJEAUhAYmKis7PzihUrZsyYUVpaeurUKR0dHbyDApIXFhbW10cikWjFihXSDAZIjZaW1r59+8rKyhYsWLBhwwY7O7vY2FjojZ44IFEAw5KVleXt7e3r66ulpZWTk3P16lUzMzO8gwIjJTAwUFZW9s3pBALB1tbW2tpa+iEBqTEyMjp16tTz58+xowJXV9fbt2/jHRSQBkgUQJ8qKyvv37/f16elpaXYwx55PF5ycnJCQoK9vb00wwPSp6Sk5O/v33tcAoZEIr3//vu4hASkjEKhnDt3Li8vz8zMbOnSpR4eHqmpqf3M3882BIwVkCiAt2tubvbx8YmMjOzu7n7to7q6uo8//tjW1vb58+dXrlx5/Pixl5cXLkEC6Vu9ejWfz39tIp/PX758OS7xAFzY2tpevXo1PT2dTCZ7eXn5+vrm5ua+OduDBw8WLFhw/vx56UcIJAgSBfAWPB5v8eLF1dXVtbW1P//8s3h6R0fH/v37p02b9vfff//8888FBQVwg8WJZuHChcrKyr2nyMjIUKlUU1NTvEICeHFzc3v48GFCQkJLS4uTk9Py5ctfvnwp/lQkEn3xxRcIobVr1/7zzz/4hQmGTQTA/xIIBMuWLSORSFgLUVFRaWpq6urqOnXqlK6urqam5r59+3g8Ht5hAtx8+OGHvc8+kEikEydO4B0UwJNQKLx69erUqVPJZHJkZGRdXZ1IJLpw4QJ2D1YZGRk5Obn09HS8wwRDBPdRAK+Lior6+eefxVdAkclkf3//J0+eNDc3f/7551999ZWamhq+EQJ8JSUl9X7gJ5FIrK+v19XVxTEkMBrw+fzff/99z549LS0tGzZsuHz5cn19vVAoRAgRiURlZeXMzEwKhYJ3mGDQIFEA/yMmJmb79u2vtQoikRgSEvLTTz8ZGhriFRgYPYRCob6+fmNjI0KISCT6+PhAxzIQ6+7u/uOPP3bu3Nna2oplCRgSiaSvr5+VlWVgYIBjeGAIYIwC+P9duXLlzSwBISQjI9PZ2QlZAsDIyMisXr0aO/sgEonCw8PxjgiMIrKyssuXL+fz+b2zBIQQn89nMBje3t4sFguv2MDQQKIA/k9ycnJfW/yenp74+Pj+L4ICE8rKlSt7enoQQmQyOSAgAO9wwOjS17Mhenp6Xr165e/v39nZKf2owJDBqQeAEELPnz93c3PjcrmvHQSIEYlEe3v77OxseEI0QAiJRCIzM7Pq6uqQkJCrV6/iHQ4YRerq6iZPntzV1dXXDCQSaeHChTdu3IAHxY0VkCiMiO7ubg6H09HR0dPTw2Kxenp62Gw2j8fr7Oxsb28XCAQCgaC9vb33Ilwu97V/LSUlpd53wSMQCOrq6gghOTk5RUVF7FN1dXUSiaSqqopNVFNTk5EZdC9RbW2ts7Nzc3Pzm9fHY8hkskgk4vP5ly5dWrly5WDLB6NTe3s7n89nsVhCobCtrQ1rk9hfPp/f0dGBEMKa7msLslgskUiUmpqanp4eFBRkaWlJJpNfu2YSIaSuro6llRoaGthfrA3LyMhgDVVNTU1WVlZJSUkqXxdIyZo1a86ePUskEvt5JASRSPzkk0+OHTs2tCpaW1u7urq4XC6Hw+nu7m5ra+Pz+W1tbeLmik3vvQjWaHtPEbdPDIlEUlFRQQgpKCjIy8urqKiQSCQNDQ1sury8vIKCwtA2sOMACe8AxpjGxkYmk8lgMOh0OpPJbGlpYbFYLBartbWV9a/W1lYul9tXCVj7Q2800ze3mG1tbb2P78X/A52dneIn+L1JVVVVXV1dXV1dQ0ND/V8aGhqampr6+vr6+vq6uroGBgaqqqriWnx9fXtnCTIyMkQiEetYVlRUnDZtmrOzs729vY2NDdx7cTTj8/lN/2IymY2Nja2tre3t7W1tbSwWq729vaOjo/1fra2t/RRFJBLFLQTbzfemrKxMJpM7OzuJRGJlZWVVVRWWBL8WDJZqiESi/s9JY3VpaGio/i81NTV1dXVNTU1dXV3tXoayaoAUBQcHUyiUoqKivLy8srIy7PhHTk5OIBCINzICgeD48eOGhoY7d+4UL8hisRoaGhobGxsaGhoaGsRb1zf1VbV4Z48dOPX+CGu04rdvHqqJt6tvJhm9iTewr8EaqqGhoY6ODtZiB7XSRjnoUXhdd3d3bW1tdXV1dXV1RUVFTU0NnU7HGm5jYyO2+0QIkclkXV1dLS2tt+6V1dXVlZSUVFVVSSSSuro6dryFJaqSipPNZmPdFXw+v729HcuvsV3Ca7kL9qK5uZnJZIp/bnl5eT09PX19/ZcvXzY3N2MTiUSihYXFzJkz7e3t7ezsbG1tTUxMJBUwGL62trba2tq6urr6+nqsZdbX12OZQWNjY0tLi3hOGRkZbW1tbO+L7XFf2w2rq6urqamRyWRVVVVsV9377wDj+fnnnzds2DCQObF04c2/3d3d4sSl/X9hjbm5ubn3joFIJIozBizlnTRpkqGhobGxsYGBgbGxMTywdFQRCoWVlZVF/3r27Jk4dcA4ODjIyclhx13i6QQCoffW9U0aGhq9O1bFW1oJRi7ub8NSXiyN6L2BfQ22ge19Vbmuri62jdXX1zcxMTE1NTU2NjYxMTE2Nh5zj+qe0IlCe3t7WVlZaWlpSUnJy5cvq6urKysrGxoasON4eXl5ExMTExMTQ0PD3sfienp6urq6Y/HpiAKBgMlkMpnM+vp6rF8kLi6OTqcTCAQej9fS0oK1cjKZbGRkZGJiYmZmRqFQpv5LTk4O728wUdDp9FevXpX/q7a2FssMxD1VCgoK2K5x0qRJ4h2nnp5e7yNvKfSRikQiKYxZ6enpESdDDAZD3GvCYDAaGhpqamoaGhrEh4AaGhqTJk0yMjIyMjKysLCYPHmyhYWFhYWFpqbmSMcJemttbcW2rmVlZdjWtaqqSrx1JZPJWJ4qEAjmzJkzc+ZMXV3d3kfkY3H4gkgkwnrysJbJZDIbGhoYDAb2z4t1vGFz6uvrY0nDlClTsK0rhUIZzfuUCZQoNDU15ebm5ufnY223tLSUTqcjhGRlZSdPnjx16lQzMzNTU1MsOTA2NtbX18c7ZKkSCAT19fVVVVVVVVXV1dU1NTXl5eVlZWVVVVVCoVBGRsbU1JRCoVAoFCsrKwcHBzs7uzdPS4PBamhoKCwsxFJVcXKAbVDk5eXNzc0tLCywnMDExMTAwMDIyMjQ0PDN0wETXENDA51Ox7pb6HR6TU0N1oCrq6ux7m51dXVx0jBlyhQbG5tp06ZJ9hh0IqupqcnPz3/+/Lk4OcBusyEnJ2dpaTllyhRTU9Peh9QT81YKjY2NNTU1WHc1tpl9+fJlWVkZdgCgoaFhaWmJbWOtra0dHBzMzc1HyeDxcZsoCIXCly9f5uXl5ebmYn/r6uoQQvr6+tOmTcOSOCsrKyw/EN+uGLypq6ur7F9Y70tJSUlbW5uMjMzkyZOnT58+ffp0BwcHBwcHIyMjvIMd7erq6oqKigoLC4uLi7EX2FgBTU1NS0tL8REw9nfSpEmjZDMxdvX09FRXV2MZmPjvy5cvsYv3Jk2aZP2/oONhILq6ugoLC/Py8vLz8/Pz83Nzc7HTXqampuLjY+yFqanpxBz9Nyg1NTXYBvbFixclJSVlZWUVFRVCoVBVVdXe3t7e3h7bwNra2uI18ndcJQocDicnJyctLY1Goz1+/Li5uZlIJJqamlpbWzs5OTk5OTk7O0/MTFbi6uvrs7OzsV1ddnZ2cXGxSCTS19d3dnb28PBwd3d3dnYec+fhRgK2ojBPnjxhMBgIIQ0NDQsLC2traxsbG+yvhYUF3pFOLPX19Vjrxf7m5+djQy8NDAyc/kWlUkdzb7CUYS0Z27pmZ2d3dnaSyWRLS0tsXdnY2Dg4OMDqkpTu7u4XL15g242ioqKcnBxsd0ahUJycnLBtrLW1tdQOJMZ8otDa2pqUlPTo0aO0tLSCggKBQGBqauru7u7m5kalUm1tbWF3JQUsFis3NzcjIyM9Pf3x48dNTU0KCgpOTk7u7u7z5s3z8PCYOEPMOBzO48ePaTTakydPsrOzGQyGjIwMtj11dnZ2dHS0tbUdZyOix4eqqqrCwsJnz549ffo0Ozu7trYWIWRhYeHk5OTi4uLh4eHs7DzRuh6Li4sTEhLS0tLS09Nra2tJJJKdnd2sWbOoVKqzs7OlpeVYHEkwFolEooqKiuzs7MePH2dkZGRnZ3d3d+vq6lKpVA8Pj3nz5k2fPn1Ee27GZKLA5/MzMjLu379///79p0+fIoScnJxmzZrl7u4+a9YsuNMw7kpLSx8/fpyWlpaWllZcXKygoODp6enn5+fn52dnZ4d3dJLHYrFoNFpKSkpqaurTp0/5fL6FhYWLi4uzs7OTk9OMGTMGfh0BGCUYDAaWMTx9+jQzM5PJZCopKbm5uXl5ec2ePdvFxWW8HoEwGIykpKSEhISEhIS6ujp1dXUPDw8qlTpr1qyZM2fCsKTRoKurC0sa0tPTaTQak8nU1taeN2+er6+vr6/vSFyqNpYSBQ6H89dff8XGxt6/f7+9vd3c3NzX19fPz8/b2xvGdo1atbW19+/fT0hISExMbGpqMjAwWLp0aUhIyJw5c8b04YhAIMjMzLxz587ff/+dn58vEomsra29vLw8PT29vLwmTZqEd4BAkkpKSrBEMDk5uaamRk5OjkqlLl68ePHixdbW1nhHJwGFhYVxcXE3btzIy8sjkUhubm4+Pj6+vr4zZ84c0/+n455IJMrLy8M2sKmpqTwej0KhBAQEvPfeezNnzpRULWMgUeByuVh+8Ndff3V3d8+ZMycwMNDX19fS0hLv0MAgCIXCZ8+e3b9/Py4u7tmzZzo6OkFBQWMuY2CxWPfv38fyg6amJgsLi8WLF3t7e3t4eMAJhQmisrIyJSUlMTGxdxtYsmTJ7Nmzx9wlxPn5+deuXYuLiysqKjIwMAgMDFy0aNHs2bOh52As6uzspNFo9+7di4uLq6ioMDMzCw4Ofu+991xdXYc7mkE0ij158uSjjz5SUlLCHmV76tSpxsZGvIMCEvDy5cvo6OgZM2YghPT09Hbs2FFRUYF3UP3hcrmXL19evHgxmUwmkUizZ8+OiYkpKirCOy6AJz6fn56evnPnTgcHB4SQiorKBx98kJiYKBAI8A7tHVpaWg4fPmxjY4MQMjY2joqKSk1NHf1hg4HLzs7esWMHdjhtZma2d+/e+vr6IZc2GhMFDodz+vRprNvE1tb26NGj2C0Fwfjz4sWL3bt3GxoaysjILF68+Pbt26NqayUQCB48eLB27Vrs1m+LFy++ePFiS0sL3nGBUaeqqurIkSMuLi4IoUmTJn311VcFBQV4B/UW6enpH3zwgYKCgoqKyscff5yeni4UCvEOCoygvLy8L7/8UktLi0wmBwcH37t3bwjb2NGVKLDZ7OjoaE1NTTk5udWrV6empuIdEZCGnp6euLg4Hx8fAoEwZcqU8+fP454ucDicY8eOYVctOjs7Hzp0iMFg4BsSGBNKSkp27dplZmaGEKJSqXFxcbg3Zszdu3exoy9HR8eTJ0+2t7fjHRGQns7OzgsXLnh6eiKErKysLl++PKhmOVoSBR6Pd+jQIT09PRUVfteDlgAAIABJREFUld27d0MXwsRUVla2du1aEolkbW197do1XI51Ghsbv/nmG21tbUVFxfXr18P5BTAEQqEwOTk5MDAQuzj25MmTPB4Pr2ASEhLc3NwQQkuWLMnIyMArDDAaPH/+PCwsjEgk2traDnwbOyoShb///tvExERBQWHLli0wCgGUlpauWLFCRkbG1dW1sLBQavVyOJxdu3YpKipqa2t/8803kK2C4SstLY2MjMSewfbbb79JOfetqKjw9fVFCM2fPx9SBCBWVFQUGhoqIyPj7Oycl5f3zvlxThTa29sjIyMJBMLKlSvr6upGuq4RLX9EjbbgX4uHwWBcvHjx0KFDz58/l1QVeXl5bm5u8vLy//3vf/l8vqSK7Ut8fLyZmZmamtpPP/3E4XBGujrR6PtNB2XMBY9vwA0NDVFRUSQSiUqlPnv2TAo1CoXCEydOqKio2NjYpKSkjGhdY64x9Damgx+m/Px8Dw8PWVnZ77//vqenp5858UwUnjx5Ym5urqOjc+3atRGt6OTJk9il7djbpKQkV1fXUT7MXuzYsWMeHh7W1tZ4B/J/XluZIpGopKTE39+/uro6ICCATCa3tbVJqi4+nx8dHS0nJ+fh4TGcIbv9YzAYixcvJhAIYWFhdDp9hGrpDRqkNI2egPPy8jw8PIhE4ueff97V1TVyFTU2Nvr4+JBIpB07dnR2do5cRdCSxzqBQHDw4EHsRrrl5eV9zYZbonD37l0lJaX58+dLYYwYn8/38PDQ19fH3l67ds3Q0PCdx74jt2calJ6eHjs7OysrK7wD+T+vrUyRSBQUFLRjxw6RSMRisS5evCjxGvPz8ykUipmZWWlpqcQLz8rKmjRpkoWFRXJyssQL7ws0SGkaVQELhcI//vhDVVXV1dV1hLLSV69eTZkyxczMLCsrayTK7w1a8vhQWlo6ffp0PT297Ozst86Az3O9UlNTg4ODQ0JC7ty5o6urO9LVEYnE3g82DA4Orqurw64h7guLxVq1atVIBzYQJBJpVN3m77WViRC6d++empoaQkhNTW0kVpqdnV1aWpqenp6vry92B35JodFo8+bNs7Oze/r0qZeXlwRL7h80SGkaVQETCIQPPvggKyuLxWJ5eXnV1NRItnw6ne7r66uqqpqRkSHBG/P1BVry+DB16tTU1FQHB4f58+cXFRW9OQMOiQKDwQgJCVm4cOFvv/02Oh+ywuFwli9fXllZiXcgYwCPx8Oe2DuitLS0/v77bxUVleXLl/P5fImU+fLly6VLl/r4+Ny8eXOU3wIcGuQ4Q6FQkpOTZWVl/f39JfjvIxAIQkNDyWTyvXv39PT0JFWsBEFLHrWUlZXj4+OtrKwCAwPZbPZrn+KQKGzdulVJSenMmTMjfePemzdvRkZGbtu2bdOmTXQ6XTy9sbHx2LFjmZmZ2Nu8vLw1a9bExMRs3rx5/fr1CKEbN26UlJQ0NTVFREQcOHAAIcRgMCIiIvbs2RMREREYGNjc3IwQys3N3bp1q4WFBYfDWbdunba2touLS3l5ubiiu3fvrl+/Pioqys3N7ddff8UmikSikydPfvrpp66urn5+fi9evBjg10lKSvLz89PU1Jw/f355efnNmzdVVFQIBMKhQ4e6u7sRQo8fPzYwMPjxxx/fWdSbgV25ckVFRcXY2Bgh1NbWtmfPHiKRiF1S1c/KPHv2bGRkJEIoNjY2IiJi//79A/wuQ6ChoXH16tWcnJzjx48PvzSRSBQeHj558uSLFy/KysoOv8CBgAb5pn5i7qdNFhYW7ty5k0Kh1NbWfvvttyYmJjY2Ng8fPuzs7Ny8efPkyZNNTEzu3bvXf8DYxLeuSSnQ09O7c+dOXV3dzp07JVXmiRMnnjx5EhsbO9J3E4eW3JdBbVqLior+85//WFtb19XVBQQEaGpquri4ZGRkDCeA4VBQULhy5Upzc/N33333+mdSPA8iEolElZWVRCLxzz//HOmKLl68SKVSsWuXm5qadHR0sBNpNBoNu+mEeAQlhUKh0WgikYjL5Xp6emITlyxZYmZmJi5tzpw5oaGh2GsHB4ewsDCRSESn0318fBBCGzZsKCwszMnJkZOTW7FiBTbbuXPnVq5cid3U4ocffkAIJSUliUSi6OjoP/74QyQS8fl8a2trfX39d46xX7BggZaW1ocffvjPP/8cP35cUVHR0NCQzWZv374dIfTkyRNstq6uLldX13eumb4C8/PzMzIyEs9mZ2dHpVL7X5nYW4TQ3r1731mvRHzxxReTJk0a/h1sbty4QSQSB3JdkKRAg3yr/mPuq00ymczw8HCEUGRkZHZ2dnt7u6urq4WFxWeffVZUVNTR0TFr1iwLC4t3BtzXmpQarFe1srJy+EUJBIJJkyZt3rx5+EX1D1pyXwa7aU1JSbG2tsYGtz58+DAuLk5LS0tRUbG+vn5oAUjEoUOHFBQUXrv/rLQThcOHD2toaHR3d49oLRwOx8DA4NKlS+IpQUFB4n3b/fv3xa0Zy9eOHj2KfSRe5M3W/OOPP2KvV69ebW9vj73esWMHQqipqQl76+HhYWlpKRKJmEymmpqaeBApk8kMCgoqKiqqq6vT09MT7+d2796NEHpn2rRgwQJDQ0Px2127diGEDh8+XFNTQyKR1q1bh02/c+fOnj17+i+qr8BEItGyZct6t2YqlYq15v5XppQTheLiYoRQenr6MMt577335s+fL5GQBgIaZD/6ilnUd5sUiUQ///wzQgh7bqdIJPrmm28QQjk5Odjbr7/+GiEkvhNGXwGL+l6T0tHT06OjoxMTEzP8otLS0hBCxcXFwy+qH9CS+zKETatIJMLuLyfeIV65cgUhtHv37qH9K0lEW1ubrKzsa8PSpX3qobi42NHRkUwmj2gtqampdDrdzs5OPKV397KioqL4NZlM9vPzi4qKWr9+PYvFWrly5VsLfPjw4Y4dO3g83m+//ZaVlcXlcrHp2NkT8UgLIyOjjo4OhBCNRhMKhebm5th0HR2duLi4adOmpaen9/T0fPzxxxEREREREfX19evWrVNQUHjnN1JVVRW/Xrt2LUIoOzvbyMgoJCTkwoUL2N766tWr7xwl1Fdg/SzS/8qUMisrKxUVlbcOtxmUgoICDw8PiYQ0ENAg+9FXzANZSkZGRrwUQki8YTExMUEIYWH0EzDqe01KB/ZA5/z8/OEXVVRUpKqqamVlNfyi+gEtuS9D2LRiX5NEIonbbWBgoKysbEFBwdD+lSQCa0XYIZmYtBOFnp6ekc4SEEIlJSWo11ajf9evXw8NDT1x4gSFQklJSXnrPAKBIDo6+oMPPpg6daqrq+s7y3z+/Dl2/4rXphcXFyspKf36v5YuXTqQOMXMzMxkZWV5PB5CaPPmzZ2dnb/88kt3dzf2xNuhBdaPQa1MKZCTk8MOVoajq6tLmukONMiR9tpTdLG3QqHwrTP3Dniwa1Li5OTkOjs7h18ObFrH3Kb1TWQy2dDQEBuvjde/EkJITk6u6/9r777jojjTB4C/20A6SK+CCChNlCKIigSsyEWlKIomcIiigPdLOYwx0cRyOXMXo8buaSwYxZJoRBNB1AjEKASQJohI7yBtKdvm98d7mdtQ1gXZHXZ5vn/w2Z3dmXl23pd3n5l95317e4WXSDtRmDBhAq5qEoW/A8rLy8V5M4vFunDhwrlz5xBC8+fP7x+eQCBYvHhxbm5uQkKCmDfRqaur9/T09Dnx7e3tVVZWrqqq6nOPX2NjozjbJNHpdCaTaW9vjxBydXX19PQ8dOjQzZs3/f39hx2YiFWGdDAlrb6+vrm5mUzbh83S0nJETuPEBBVyVCEDHsaRHHE5OTl4LuA3ZG5u3tLS0tDQ8OabEgFq8lADG1IACCEOh2NjYzOMAEYKl8t9/vx5nzZW2onCokWLysvL09PTJboXR0dHhNDly5fJJQKBgM/n939nb2/vkSNHEEKhoaGPHj0SCAQpKSkIITqdTt4i8vjx4zt37vj4+OCn4qSNLi4uCKFPPvmEPKfJzMz87rvvHBwcCIKIi4sj3/nixYuhduMvKyvjcrnBwcH46QcffFBTU/P+++8HBQW9dt3BAkMIMZnMzs5O8ih1dnbi94g+mG+YQQ/VxYsXlZSU3rxNX7Zs2fXr1yXdqpKgQg7PYHXyDZEBD+NIjqz79+8XFxcvW7bszTfl5eWlpKSE/5clB2ryUANDQ6nGDQ0NdXV1AQEBwwhgpCQmJra1tS1atOhPS6XTP0KYp6enl5eXpKde9fLyYjAYR44cYbPZjx8/NjIyQgjFx8ez2eyrV68ihI4ePUoQRE9Pj4ODA55KgMPh6Ojo4I5yGzZsQAhlZGTcv38f1+/Zs2c/ffr09OnTDg4OqqqqOTk5dXV1MTExSKjHjbe3t4aGBp73BR9ob2/vb7755sMPP/T39+dyuQKBAI+Csnz58nPnzh06dMjHx+e182D5+fnp6+vjTtoCgSA8PFy48yCPxzM1NX377bfFPDIDBkYQBL4lZufOnUVFRTt37rSystLU1MQDdYk4mPiH3q1btw6xfIajpaXF0NBwRPp1d3Z2mpiYrF69+s03JSaokIMREbOIOrl3716EUHZ2Nl7r3//+N0Lo3r17+Om+ffsQQuQwc4MF/OjRo8GOpJjBv4muri4HB4eFCxeO1Abfe+89AwOD5ubmkdrggKAmD2YYTWtERASNRiP75MbGxq5du3bYAby5np4eOzu7gICAPsspSBSePHnCYrHInq4S0traGhYWpq+vb2ZmtmPHjsjIyLCwsOTk5Lt3786dOxch5ObmlpSU1NPT4+rq6ufnt3fv3sjIyOPHj+PVc3JyTExMrK2tL1++TBDEhg0b1NTU3N3dk5OTExMTdXR0AgMDf/zxR3x9ZuPGjQ0NDefPn1dRUUEI7dixg8fjsdnsqKgoY2NjfX39qKio1tZWvOXm5ubVq1fr6enp6uquXbtWnKmwcnJyVq5cuXDhwsjIyM2bN/efGmP9+vU4TnEMFlhbW5u/v7+qqqq7u/uTJ0/Cw8PffffdxMREEQczIyMDd7GxsLCIj48nNyUJfD4/ICDAyMiIbDve0K1bt+h0+oh0OBcHVMgBpaSkiIh5sDqZkpKCT21Xr15dUlLy4MEDJycnhNDixYufPn2alpY2ffp0/OqLFy9EBzzgkcTfHBLF4/FWrFgxfvx4EQPsD1Vzc7Opqam/v79EJ1GDmjyYYTStERERLBYrLCwsMDAwIiLis88+63MKPaQA3lxUVJS6ujr+rxFGzVwPBw4coNFo//nPfyjZu/xxdXWlcLZ7KeDz+Rs2bFBUVLx///4Ibnb//v00Gm3nzp1SnvxX7sl9hXxDXV1dgYGBysrKKSkpI7vl1NRUJSWlsLAwKUy4OhZIuiZHRESMGzeOwgCEbdu2jcFgDDhHIzUjKMfExDQ3N0dERFRXV2/btq1Pj+UxyMTEZLBuL2fPnu37c9GfpaSkvPXWW+PGjRuRrY1CXV1d77777o0bNy5evOjl5TWCW46NjVVQUIiJicnOzj5x4sQoH8hZmqBCSk5xcfGKFSsqKip++uknPELRCPL09Lx69WpAQEBjY+P58+fxJCxjmUzX5P4BSEhvb290dPTp06ePHTtG9pD4E+mkKgM6fPgwi8Xy9vaWlWlJRxU8qldwcPCUKVNe+1Oc7EpLS7OystLW1h7ZawnCHjx4YGRkpKure+rUKbi0MGxjpEK+ia6urm3btikqKrq4uIzgLw79/frrrwYGBqampj///LPk9iKvpFmTly9fTqfTOzo6qAqAIIgnT57Y2dmpqanduHFjsPdQmSgQBJGZmWlvb6+mpnbixAloo4ckPz9/4sSJUp4fWZp6enri4uIYDMbixYslPS9tW1vb5s2bmUzmzJkzf//9d4nuS17JfYV8Q9evX7ewsFBXV//6669xHzeJamxsDA4OptFokZGRbW1tkt6dPJFaTd6yZQvuexEWFvbw4UPpB9DT07Nt2zYmk+nj4yN6HHGKEwWCIDgczvbt2xkMhoODQ0JCAtXhAOpxOJwzZ85MnDhRWVn566+/lloGmZOT4+npSaPRfH19RSTXAIiPz+ffuHHD3d0dIbRkyZKKigpp7j0hIUFXV1dbW3v79u2QLgASbmMtLS2VlZW/+OKL196ESH2igGVnZ+NRtDw9PUe8gw+QFTwe7+zZs5aWlgoKChs3bhSn3/LIEggEN27cwL8cu7m5Xb58GTqFgeHp7Ow8cOCAhYUFg8EIDg4mJ/iRsubm5o8++khVVVVPT+/f//53V1cXJWGAUYLL5f7nP/+xsLDAbWxlZaU4a42WRAH77bfflixZghBydnY+duyYFG5SAqNEXV3dF198YWFhQafTg4KCSkpKqI0nMzNzzZo1DAbDyMgoNjY2IyOD2niArODz+Q8fPoyMjFRXV1dUVFyzZs2zZ8+oDopoamravn27urq6hoZGZGRkbm4u1REBaauurv7iiy/MzMxYLNaaNWuG1MaOrkQBS09PDw0NVVRU1NTUjI2NxRNwAbkkEAju37+/cuVKBQWF8ePHv/fee8+fP6c6qP8pKSn55JNP8C3dU6dO/de//iX9ixxAVuTl5W3ZssXU1BQh5Orqun///pEa82Ok1NfX796929zcnEajeXt7X7x4sbe3l+qggGTxeLzExER/f38Gg6Gvr79ly5ZhTGs+GhMF7NWrV8eOHbO1tUUI2drabt++XdIzqAJpysvL2759+6RJk8gLSK+dPJ5CGRkZsbGxOjo6uDbGxcU9fPhQ0qOLgtGPx+M9fPgwLi4Ot1QmJiaxsbHkeJGjE5/PT0pKCgoKYrFYmpqaQUFBZ86cgcu3cgbXzNjYWENDQ9zGnjlzZth5IY2Q7tjmQyUQCB48eJCQkHDt2rWGhgYnJ6egoKClS5fif0sgW/h8/m+//Xbt2rUrV66Ul5dbWVkFBQWtWLECj7I3+vX09CQlJSUmJiYmJlZVVRkaGi5evNjPz2/u3LkwBsOYUllZiUcSvHPnTkdHh729vZ+fn7+/v4eHBznz9ehXXV195cqVK1eupKenq6io+Pn5BQQEzJs3D0ZfkF1dXV3379+/evXq9evXm5ubp0+fHhgYGBwcbGlp+SabHe2JAkkgEKSnp1+6dOn8+fOtra16enpeXl6+vr7+/v44YwKjVl1d3Z07d27evHn37t2WlhZzc/O//OUvQUFBs2bNojq04cvPz79582ZycvL9+/d5PN7EiRN9fX09PT29vb3xxWcgZ2pqatLS0pKTk1NTUwsKCsaNGzdr1ixfX9+lS5fi6f5kV1NT061bty5fvvzTTz8RBOHk5OTr6+vr6ztnzhxpzsYOho1sjlJTU3t6emxtbYOCglatWmVtbT0i25eZRKG+vv7EiRNHjhypr6+fPXu2i4vLo0eP8KRkzs7OPj4+np6eHh4e2traVEcKEEKooqIiLS0tLS0tKSmpuLhYRUXFy8tr/vz5CxYsmDx5MtXRjaTm5ubU1NQHDx788ssv2dnZfD5/8uTJs2bNmjFjhrOzs729PYvFojpGMBxsNjsrKyszM/O333578OBBTU2NkpLSjBkzvLy85syZ4+7urqysTHWMI6ypqenu3btJSUlJSUkVFRXq6ure3t5eXl4eHh7Tp0+HpGH04PF4eXl56enpDx8+TE5Obmpq0tfX9/HxmT9/vq+vr7Gx8cjuTgYShSdPnhw8eDAhIUFVVTUiImLjxo1mZmb4pba2tpSUlDt37ty/f7+oqAghZGNj4+HhMXPmzJkzZ06ZMgUGh5YaLpf7+++///rrr+np6enp6dXV1Uwm08nJCdddT09PRUVFqmOUuPb29rS0tF9++SU1NTUrK4vNZisqKjo6Orq4uDg7Ozs7O9vZ2UHeMGqx2ezs7OzMzMyMjIzMzMyioiI+n6+tre3q6jp79uw5c+a4ubmNnS/LoqKipKSk5OTktLS0pqYmRUVFZ2dnd3f3mTNnenh44EkjgTQ1NTX9+uuvjx49Sk9Pz8jI6OzsVFdX9/Dw8PX1nTdvnqOjo+S+70Z1opCamvrPf/7z5s2bTk5OUVFRoaGhIlL49vb2x48fp6amZmZmpqamtra2qqmpWVtb29ra4jZ6+vTp8ncGQKH29vanT58WFBTk5+dnZmb+/vvv3d3d6urqbm5unp6ezs7Oc+bMGcs/dvL5/GfPnmX+ITs7m81ms1gsU1NTW1tbOzs7/NfOzk4KY7mD/jgczvPnz3EFxn9xZqChoWFvb+/8B1tbWzjfwD+74Nb1yZMnHA5HS0uLbFrt7Ozs7e3HwpmAlNXU1ODWA9dP3J1/4sSJuIGdNWvWtGnTpNMnZjQmCgRBJCYm7t69+9GjR76+vh9//DGevVR8PB4vJycnIyMjOzs7JycnNze3s7OTyWTa2Ng4Ojra2tpaWVlZW1tbW1vjETTBa7W0tBQXFz979qy4uLiwsDAnJ+fly5cIIR0dHScnp6lTpzo5Obm6usr6j7WSw+fzCwsLs7Oz8T98fn7+y5cv+Xw+i8WysrKys7ObNGnSxD+YmpoyGAyqQ5Yfvb29ZWVlL168KC0tLS0tLS4uLigowDeJKSoq2tjYTJkyxd7efsqUKdOnT8d3w4LBdHR0PHnyJCsrKycnB58qcLlcRUVFe3t7R0dH3K5aWVlZWVlBBiw+Dofz8uXLoqKi4uLi4uLi3Nzc3NxcNpvNYDCsra0dHR2dnJycnJxmzJhBSb/p0ZUoCASCxMTEzz//PCMjw9fXd+fOnXjo0zffbElJSU5OTnZ29tOnT4uKil6+fMnj8RBCpqam1n+wsLAwMzMzMzMbyx0dCIKoq6srLy8vLy8vKysrLi4uKioqKipqampCCI0bN87a2trGxgYnB1OnTjUxMaE6ZFnV09Pz7NmzwsLCvLy8Z8+elZSUlJaWdnZ2IoQUFBQmTJhA5g0mJiYmJibGxsZGRkZw3iZCV1dXRUVFbW1tVVVVRUVFaWkpTg6qq6sFAgFCSEdHZ+LEiZaWlvb29pMnT7a3t584cSKTSc0kuvKBw+HgM4enT5/m5uYWFxdXVFQIBAI6nW5mZoZPyaysrMzNzaF1xdra2ioqKnAbW1JSUlRU9Pz587KyMvyVZGJiYm1tbWdnh5MDOzs7JSUlqkMeTYnCrVu34uLiCgoKAgICPv7446lTp0puX1wut7S0FJ8f4+/C58+f19XV4VdVVVUnTJgwYcIEMzOzCRMmGBkZ6enp4b+6urpycKrX29vb2NhYW1tbX1/f0NBQWVlZXl6O625lZSWeR5XBYOAqa21tPXnyZPzAzMxMhu7+kkUNDQ2l/dTW1uJGBCGkr69vaGhI5g3GxsY6f9DT05PvuzQFAkHTHxobG+vr6+vr68nMoKqqqq2tDb9z3LhxJiYmE//M0tJSXV2d2o8wFvT29j5//hw3rc+fPy8qKnrx4gXZuiorK5NJg6mpqampqa6uroGBgb6+vq6urnx0AeHxeI2NjY2NjTU1NfhvRUVFRUVFWVlZRUVFe3s7fpu2tralpaWNjQ15GWbUXuQeFYlCZmbm3//+95SUlGXLlu3evXvKlCmUhNHT04O/L8l0D6utrSXnIKfT6bq6unp6erhmjx8/XktLS1NTE/8lH2hpaampqUn/I7x69aq1tVX4L/mgubm5tra2oaGhvr6+paWFXEVFRcXExITMikjGxsZwpjVKCASCurq6qqqqmpqaysrKmpqa6urqysrK2trampqajo4O8p1MJrNP3qCpqamhoaGuro6H78UPyIWU96zs7u5u/8OrV6/Ix21tbe3t7a2trU1CmpubhdsrbW1tPJmyoaGhqampsbGxsbGxiYkJnjScwg8F+uvp6akQgr8yKyoqampquru7ybdpa2vjjMHQ0HD8+PHCTWufx9I/YSMIQrhdJZHtLT71wikCWVEVFRUNDAzMzMzI9AgzNzeXoT5zFCcKlZWVu3btOnnypIuLy5dffjlnzhwKgxGhtbW1rq6uoaGhrq4On4XjL92WlhayrnR1dfVZS1lZWVFRUV1dnclkampqslgsVVVVJSUl/NOdgoKCcPJIo9E0NTXJpwKBgDw9wtra2vDl087OTi6X29rayuPx2tvbe3t7u7q62Gw2h8PpEwD+SiBzF3xRRE9Pz9DQUE9PT19f38DAYHQmsEB8vb29+Hu0oaGhsbGR/FptaGjAlRN/6ba3t/f09PRfXUNDg06nD/YXIYTrrfAqioqKfdq49vZ2Pp8vvISsrlwuF9dY8i+Hw8HVlc1m949HRUVFOLPREYJzdB0dHW1tbR0dHUhk5UNnZyduTslTcNzAkq0rrsN91mIwGHg2DWVlZbKlZTAY5EU1NTU14Roybtw44Wv4uNkknwq3t21tbXw+v7W1FVfXnp6e7u5uXHv7xKCmpkYmLuPHj8ctqvAFEgMDA+FWXXZRlih0dHR8/vnnBw8enDBhwp49e5YvXy7rXYs5HI5wvtne3t7d3d3T09Pe3s7j8VpbW3HL2NXVha9PkA8wXCmFN0i21JiKioqCgkJJSYmKioqtra2GhgaTydTQ0MD/KvhVLS0t4Wsb8DMBEMbhcMhzd5w9cLlc/B2Pv9fx39bWVoIgXr16hdfqU1ERQvhrHicohoaGdDodt9TC78EVEiHEZDLV1NRwtoH/4hQZ11slJSX1P2hpaeG2XmoHBMiK/mfzuDILnyaRX/B4FbICY/g9+PxKR0cH5xnCbyDbW5xhaGlp4aqLMwxcbym/qkEVahKFH374ISYmpru7+7PPPouMjKT8+qcMiYmJuX37dnFxMSQBgFpJSUnz589vaWmR744RQJ6sX7++tLQ0KSmJ6kBkjLS/bGpqaoKCgpYtW+bh4VFYWLhp0ybIEoYkNja2tLT09u3bVAcCAABgTJBeoiAQCI4fPz558uSsrKw7d+4kJCRAh6NhsLKy8vX1/eabb6gOBAAAwJggpUShoKDAzc0tJiaB4E01AAAWLklEQVTmb3/7W15e3rx586SzX7kUHR39888/4yGrAQAAAImSRqJw9OhRFxcXFouVnZ39+eefw3Bdb2jJkiXm5uZHjhyhOhAAAADyT7KJQmtr68qVKzdt2rRu3bpffvmFqgES5AydTo+Kijp16hQ5cAcAAAAgIRJMFFJSUuzt7dPT01NSUvbv3w+dFkdQREQEn8+Pj4+nOhAAAAByTiKJAp/P37Jly7x582bOnJmTk+Pl5SWJvYxlWlpaISEhBw8eHA0DawIAAJBjI58otLW1LVmy5MCBAydOnEhISIB7rCUkJiamsLDw3r17VAcCAABAno1wolBaWoqvIjx48CA8PHxkNw6ETZ06ddasWXCfJAAAAIkayUQhNTXVw8NDQUHh0aNHrq6uI7hlMKDo6OgbN268fPmS6kAAAADIrRFLFE6ePOnj4zNnzpy0tDQzM7OR2iwQISAgwNDQ8Pjx41QHAgAAQG6NQKJAEERcXFxkZORHH32UkJAgQ1Nnyjomk7lu3bqTJ08Kz9MKAAAAjKA3TRT4fH5kZOS+ffvOnTu3Y8cOWZ8BUuZs2LCho6Pj4sWLVAcCAABAPr1RosDlckNCQuLj469du7Z69eqRigmIT09PLzAw8ODBg1QHAgAAQD4NP1Hg8XghISE//fTT7du3lyxZMoIxgSGJjo7OyspKT0+nOhAAAAByaJiJAp/Pf+edd27fvn3jxg0YT4la7u7urq6ucJ8kAAAASRhOokAQRHh4+A8//JCYmDh37tyRDgkM2aZNm65cuVJdXU11IAAAAOTNcBKFLVu2XLx48dq1a5AljBIrV67U0tI6efIk1YEAAACQN0NOFL7++usvv/zy1KlTCxYskERAYBgUFRX/+te/Hjt2jMPhUB0LAAAAuTK0ROHGjRvvv//+v/71L7jHYbSJiopqbGy8du0a1YEAAACQK0NIFAoLC9euXfvOO++89957kgsIDI+pqelf/vIX6NIIAABgZImbKLS0tPj5+Tk6Oh49elSiAYFhi46OTktLy8jIoDoQAAAA8kPcRGHTpk1cLvfq1asKCgoSDQgMm7e3t4ODw5EjR6gOBAAAgPwQK1H4/vvvL126dOLECV1d3T4vdXR0SCAqeTa8I9ZnrYaGhgsXLuzfvz8/P194+caNG+Pj4xsaGiQdzygnuQ8lZkGMZfJUo6C4xwiotKK9PlFoaGjYsGFDVFTUwoULhZcfO3bMy8trypQp+GlKSoq7u3tZWdmwQ5FFPB7v4cOHH3/88c8//4yXiDgOfY6YmPqvVVRUFBERMXv27Hv37k2bNq29vZ18ae3atcrKyqdPnx7GluWjBA8dOjR79mx3d/cR3/KQCmJskqcaBcU9RkClFcfrE4UPPvhATU1t7969fZZHREQIBAI+n4+fvnr1qrKyks1mi95abW3t8AIdnZ48eXL69Ok9e/ZUVVXhJSKOQ58jJqb+a23dutXe3t7U1PTMmTPffvuturo6+ZKysvI777xz+PBhcfYilyW4fv36trY2gUAw4lseUkGMTfJUo6C4xwiotOJ4TaLw/Pnz7777bteuXSoqKn1eYjAYJiYm5NOAgIDq6mo7OzsRW2ttbV21atXwAh2dPDw8YmJihJeIOA59jpiY+q/1888/a2hoIIQ0NDT6H8/o6Oiqqqoff/xxqFuWjxJkMpnGxsaS2PJQC2IMkqcaBcU9RkClFcdrEoXt27dbW1sHBwcPewckNpsdHBwso5d0RJBy787u7m7R2a6lpeWCBQskcZ+kvJbg8Ly2IMBryVCNguIG2NistEwRrxUWFiYkJFy4cIFO/18+cf369cTERC0tre7ubuHLL42NjZcuXXJ1dZ0xYwZCKCcnZ9++fba2trW1tb29vYcPH/7++++fPXv26tWrdevW2djYfPDBB/X19du2bTMzM6uoqGhqajp58qS2tnZ2dnZ8fPzVq1dzc3M3b978ww8/TJw48eLFixMnTsQ7unXr1s2bN1ks1uPHj8PDw9etW4cQIgji2LFjOTk5v//+u4aGxqFDh6ysrER/8gFXuXTpUkREhKamZmVlZVtb24EDB3bs2OHm5vbrr7+K2LuwPsdBxBETbcC1zpw5k5ycjBC6fPlySUnJpEmT4uLi+q8bHR3t5+eXl5dnb28v5pb7Rz76S3DAvZOv3r1795///GdGRoarq+uRI0fw3vt/KIkWxBghNzVqsM8CxS1/oNIODTG42NhYKysrPp9PLomPj3d3d+/u7iYIoqmpSVdX18DAgCCI1NTU2bNnI4SuXLmC32ljY5OamkoQRFdX1+zZs/HCJUuWmJubk1ubO3fuihUr8OOpU6eGhoYSBFFbW+vr64sQ2rRpU35+flZWlqKi4sqVK/Hbzp49GxISgkPavXs3Quju3bsEQfzjH//49ttvCYLg8Xi2trYGBgZsNlvERxOxyvz5801MTMi3OTg4uLu7i9h7Xl4eQujkyZMDHofBjphoItZqampCCO3atUvE6gKBwNraeuPGjeJvWRZLcMC9EwSxcOFCbW3t8PDwn3766fDhw8rKykZGRp2dnYN9KBHesCDk2507dxBCx48fl5saBcUt9yIjI319feWpGZROpR00Uejt7dXV1d29eze5hM1mGxoaXrhwgVyyfPlyMibcauDji2ccOHjwIH6JXKX/8d2zZw9+vHr1akdHR/z4o48+Qgg1NTXhp7NmzbKysiIIoqGhQUNDo7S0FC9vaGhYvnx5QUFBdXW1vr4+mdB8+umnCKGLFy+K+NgiVlm6dKlwouDu7o4ThcH2Lpwo9DkOoo/YYESvJWbZf/XVV6qqqq2treJvWbZKUMTeFy5caGRkRL5t27ZtCKH9+/cP9qEGMyIFIcdwhdHX15ePGgXFPRZERkZ6e3vLTTMotUo76E8PP/74Y3Nz85o1a8glDx8+rK2tdXBwIJcI/zyvrKxMPmaxWPPnz9+8eXNBQcGePXtCQkIG3MW9e/cQQt3d3fHx8Y8fPyYIAi9nMBgIISbzv7GZmJiUlJQghFJTUwUCgYWFBV6uq6t79epVXKhcLnf9+vXkliMiIpSUlAb7aAih9PT0oa4y2N773JwqfBxEH7HBDG+tPsLCwj799NMzZ87ExsaKuWXZKkERe0cICXfuDQsL27VrV2ZmppgfijQiBSH36uvr5aNGQXGPEa2trXLTDEqt0g6aKJw9e9bX19fU1JRc8uzZM4QQi8USZ7vXrl1bt27dkSNHrl69evny5Tlz5vR/D5/P37t3b1ZWVnR09IwZMx49eiR6m3l5eVwulyAIGo0mvLywsFBFReXEiRPiBDbsVQbbuwhDOmJvuFYfmpqaoaGhBw8ejI6OJruYyFMJir93c3NzBQWF7u5uMT8UaUQKYiyQjxoFxT1G4P59UGmHZOC7Hnp7e5OTk4OCgoQX4lSlvLxcnO2yWKwLFy6cO3cOITR//nz8eYQJBILFixfn5uYmJCSIbq9J6urqPT09BQUFfUJVVlauqqoiRzLAGhsbRWxqGKsMtncRqwzpiL3hWv3FxMS8ePEiKSlpeFse5SUo/t7pdDqTycT9Ol/7oYSNVEHIPfmoUVDcYwQ+cYJKOyQDJwqpqaldXV24LwbJ0dERIXT58mVyyWDDB/X29uIZB0JDQx89eiQQCFJSUhBCdDq9s7MTv+fx48d37tzx8fHBT3GGJTpWFxcXhNAnn3xCDqeTmZn53XffOTg4EAQh3KXzxYsXoju0i1iFyWR2dnaSn6uzsxPvbrC9i9iL+EdM/LVee5RItra2c+fOFb5PUp5KUPy9l5WVcbnc4ODgwT7UYEaqIOSefNQoKO4xQlVVFUGlHaoBey7ExcXZ2Nj0X+7l5cVgMI4cOcJmsx8/fmxkZIQQio+PZ7PZ+FeWo0ePEgTR09Pj4ODA4/EIguBwODo6Ounp6QRBbNiwASGUkZFx//59fMRnz5799OnT06dPOzg4qKqq5uTk1NXV4SGMyD4g3t7eGhoaAoGAIIhFixYhhLy9vb/55psPP/zQ39+fy+UKBAJXV1eE0PLly8+dO3fo0CEfH5/GxkYRXTNErPLZZ58hhHbu3FlUVLRz504rKytNTc3MzMzB9p6WloYQ2r9/P96y8HEQfcREhCdirczMTITQ1q1bRaxOunr1Ko1GKyoqkr8SxNf3Bty7n5+fvr4+vs1BIBCEh4fj7jyDfSgpFIRcwt2+PD095aNGEVDcYwC+60FumkFCWpV24ETB2dk5Ojq6//LW1tawsDB9fX0zM7MdO3ZERkaGhYUlJyffvXt37ty5CCE3N7ekpKSenh5XV1c/P7+9e/dGRkYeP34cr56Tk2NiYmJtbX358mWCIDZs2KCmpubu7p6cnJyYmKijoxMYGPjjjz/iXh4bN25saGg4f/48HhRyx44dPB6PzWZHRUUZGxvr6+tHRUWRvfqbm5tXr16tp6enq6u7du3a6urq137ywVZpa2vz9/dXVVV1d3d/8uRJeHj4u+++m5iYSBBE/73/9ttveAoMZ2fnW7du3bt3T/g4iDhiwjedin+cMzIy8OhaFhYW8fHxfW5q6I/H45mbm7///vtyWYID7r2zszMnJ2flypULFy6MjIzcvHkzeafTYB9KCgUhl3CiUFZWJjc1Copb7uFEQZ6aQelUWhrR7+pEb2+vmprauXPnVqxYMeQLFGCU+cc//rF3796qqqr+g3AD8CaSkpLmz5/f0tKipaVFdSwAiGX9+vWlpaXCPbeAOAa46yE3N5fL5U6bNk360YwsExOTwTobnj17Fl/8oYrUYlu3bt3nn38eHx8fGRk5UtuUGikcpdFcScCIg+IGMmc0VNoBEoWsrCxVVdVJkyZJYfcS1af76Kgitdh0dHRWrFhx4MCBdevWiX9j5yghhaM0misJGHFQ3EDmjIZKO8BdD9nZ2Y6OjsLzOwCZtnnz5vz8/IcPH1IdCAAAANkzQDZQUlJiY2Mj/VCAhEybNs3d3V0S80kCAACQewMkCuXl5RMmTJB+KEByoqOjv//++9FwCQsAAIBs6ZsoEARRWVkJiYKcCQoK0tHROXbsGNWBAAAAkDF9E4XGxsauri4zMzNKogESoqCgEBkZeezYsZ6eHqpjAQAAIEsGSBQQQgYGBlQEAyRow4YNra2twoN9AgAAAK/VN1F49eoVQkhTU5OKYIAEGRoaLl++fP/+/VQHAgAAQJYMnCjAUGtyKTo6OjMz8/Hjx1QHAgAAQGb0TRTa2toUFBSUlJQoiQZI1KxZs5ydneE+SQAAAOLrmyj09vYqKipSEgqQgqioqEuXLtXX11MdCAAAANnQN1Hg8XhM5gDjOgP5sGrVKjU1tZMnT1IdCAAAANnQNycgEwU2m93c3NzQ0IAQcnFxoSA0IAFKSkrh4eGHDx/++9//zmKxEEIZGRnffPPNZ599BoNnANE6Oztv375NPs3Pz1dRUbl+/To5MSmLxVq6dClF0QEwgMLCwry8PPJpdXV1W1ub8M1fFhYW8AX3WrTS0tJz5841Nzc3NTXV1dW9fPmyqampt7eXw+Hgd2zcuPHQoUPURglGUHl5uaWl5fnz5/l8/r59+zIzMxFCmZmZ06dPpzo0MKrx+XxjY2MRv1stWrTo1q1b0gwJANEePHgwd+5cEW84depUWFiYtMKRVUw9Pb2vvvqqo6MDISQQCPq/w8fHR+pRAQlisVgODg4RERHd3d3kfJL4bhcARGAwGCEhIYcOHeJyuf1fpdPpq1evln5UAIgwZ84cQ0PD2traAV9lsVjLly+XckiyiK6iohIVFcVgMAbMEuh0uuh0DMiQzMzM0NBQMzOzgoICNpstEAj4fD5+qbW1ldrYgEwICQkZMEtACLFYrLffflvK8QAgGo1GW716tYKCQv+XmEymn5+fhoaG9KOSOXSEUExMDEEQA77s4OAwfvx46YYERl53d7eHh4eLi0tCQgKfzyd/V8KYTCYkCkAcbm5uA/ZlYTKZb7/9tqqqqvRDAkC0kJCQPi0exufzQ0NDpR+PLKIjhIyMjAIDA3HXNmEKCgqLFi2iIiowwpSUlLZs2UKn0we7aAw/PQAxrV27tn9bwefz4XcHMDpNnz590qRJ/ZcrKSktXrxY+vHIov/eHvnBBx/0/wrhcDjQQUFuvP32299++y3ZKUEYjUaDKwpATKtWrerfVqiqqi5YsICSeAB4rdDQ0D7ZLYvFCgwMhKEFxfTfRMHZ2XnGjBkMBkP4NRaL5enpSUVUQCLWrFmza9eu/rmCQCCARAGIafLkyXZ2dsK1iMViBQcHw0BtYNQKDQ3tk91yuVy4Bia+/w249OGHHwr3Z6TRaDNnzoSES85s3bp18+bNdPqfBtri8/mQKADxrV27Vvikgsvlrlq1isJ4ABDN0tJy6tSpwtmtpqbmW2+9RWFIsuV/XxhLly41MTEhDyWLxYJriXLpq6++WrVqlfD4mwKBoKmpicKQgGwJCQkh75dBCOno6Hh5eVEYDwCvJZzdslisNWvWwBjE4vtfosBgMP7v//6PPNeEDgryikajnTp1ysvLS/j/pLm5mcKQgGwxNTV1d3fHbQVuc/v8agnAaBMSEkJeMudyuSEhIdTGI1v+dAk6IiJi3Lhx+LGqqqqzszMVIQGJY7FYN27cmDZtGtnBB+56AEOyZs0afPUR2lwgEwwNDT09PXF2a2Rk5O7uTnVEsuRPiYKamtq6detYLBadTn/rrbfgLEGOKSsr37p1y9TUFF9XgD4KYEgCAwPxAzMzM1dXV2qDAUAcOLtlsVhr164d8P4vMJi+s0fGxsby+XyBQDBv3jxKAgJSo6Ojk5ycrKWlRaPR8BjeAIhJV1cX/zT57rvvUh0LAGIJDAzEY8lA39uh6psoWFhYzJw5EyHU3Nzc3t5ORUhAeiwsLJKTk5WVlTkczqeffnrz5s3BxugEQFhvb6+5uTlCyMDAAOoMkAkdHR1WVla6urpwsXyoaH3+yePi4vbu3Uuj0RgMxvjx47Ozsw0NDakKDkgBm822s7MrLy9XUFDgcrkBAQHCc7AC0B+bzXZ1dS0uLiYIgiAIqDNg9EtNTfX19eXz+fgrLz4+fsWKFVQHJTP+dEWhrKzsyy+/RAgRBMHj8V69erVnzx6KAgNScvTo0ZqaGoQQh8MhCOLKlSsPHjygOigwqh09erSkpAT/Rgl1BsiE2NhYHo/H4/Fwvd24cSPVEcmSPyUKhYWFwhcYuFxuVlaW1EMCUpWfny880BaLxcrPz6cwHjD6QZ0BMufZs2fk4B8EQbS0tDQ2NlIbkgz5U6JgZWUl/JTFYtnZ2Uk3HiBt1tbWwgM1crlca2trCuMBox/UGSBzLC0thSuthoaGjo4OhfHIlj8lCpMmTVq/fj2NRlNQUGCxWCoqKlu3bqUqMiAdUVFRRkZGLBZLQUGBwWAsWLAABtoCokGdATLnq6++wl9tCgoKNBpt3759cIek+Pp2ZkQIXb9+/ZdfftHX1w8LC9PV1aUkLCBNnZ2dp06devnypYuLy8qVK6FLMHgtqDNA5hQWFl68eJHD4SxbtszNzY3qcGTJAIkCAAAAAADWdxwFAAAAAAASJAoAAAAAGBQkCgAAAAAY1P8D40DipyoAw78AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# For comparison to above re-use points dataframe instead\n",
+    "# of rand generating each time when running the task-graph.\n",
+    "points_tspec.update({\n",
+    "    TaskSpecSchema.load: {\n",
+    "        'points_df_out': points_df\n",
+    "    }\n",
+    "})\n",
+    "\n",
+    "numba_distance_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'distance_by_numba',\n",
+    "    TaskSpecSchema.node_type: NumbaDistanceNode,\n",
+    "    TaskSpecSchema.conf: {},    \n",
+    "    TaskSpecSchema.inputs: {\n",
+    "        'points_df_in': 'points_task.points_df_out'\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "cupy_distance_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'distance_by_cupy',\n",
+    "    TaskSpecSchema.node_type: CupyDistanceNode,\n",
+    "    TaskSpecSchema.conf: {},\n",
+    "    TaskSpecSchema.inputs: {\n",
+    "        'points_df_in': 'points_task.points_df_out'\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "task_list = [\n",
+    "    points_tspec,\n",
+    "    cudf_distance_tspec,\n",
+    "    numba_distance_tspec,\n",
+    "    cupy_distance_tspec\n",
+    "]\n",
+    "task_graph = TaskGraph(task_list)\n",
+    "\n",
+    "task_graph.draw(show='ipynb', show_ports=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we run the tasks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "out_list = [\n",
+    "    'distance_by_cudf.distance_euclid_df',\n",
+    "    'distance_by_numba.distance_df',\n",
+    "    'distance_by_cupy.distance_df'\n",
+    "]\n",
+    "(df_w_cudf, df_w_numba, df_w_cupy) = task_graph.run(out_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HEAD df_w_cudf:\n",
+      "          x         y  distance_cudf\n",
+      "0  0.887968  0.582714       1.062094\n",
+      "1  0.146722  0.296758       0.331048\n",
+      "2  0.391815  0.623228       0.736161\n",
+      "3  0.882974  0.621067       1.079522\n",
+      "4  0.794594  0.844349       1.159442\n",
+      "\n",
+      "HEAD df_w_numba:\n",
+      "          x         y  distance_numba\n",
+      "0  0.887968  0.582714        1.062094\n",
+      "1  0.146722  0.296758        0.331048\n",
+      "2  0.391815  0.623228        0.736161\n",
+      "3  0.882974  0.621067        1.079522\n",
+      "4  0.794594  0.844349        1.159442\n",
+      "\n",
+      "HEAD df_w_cupy:\n",
+      "          x         y  distance_cupy\n",
+      "0  0.887968  0.582714       1.062094\n",
+      "1  0.146722  0.296758       0.331048\n",
+      "2  0.391815  0.623228       0.736161\n",
+      "3  0.882974  0.621067       1.079522\n",
+      "4  0.794594  0.844349       1.159442\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('HEAD df_w_cudf:\\n{}\\n'.format(df_w_cudf.head()))\n",
+    "print('HEAD df_w_numba:\\n{}\\n'.format(df_w_numba.head()))\n",
+    "print('HEAD df_w_cupy:\\n{}\\n'.format(df_w_cupy.head()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use `verify` function defined above to verify the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Max Difference cudf to numba: 2.220446049250313e-16\n",
+      "Max Difference cudf to cupy: 2.220446049250313e-16\n"
+     ]
+    }
+   ],
+   "source": [
+    "mdiff = verify(df_w_cudf['distance_cudf'], df_w_numba['distance_numba'])\n",
+    "print('Max Difference cudf to numba: {}'.format(mdiff))\n",
+    "mdiff = verify(df_w_cudf['distance_cudf'], df_w_cupy['distance_cupy'])\n",
+    "print('Max Difference cudf to cupy: {}'.format(mdiff))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To illustrate multi-input nodes let's create a verify node."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class VerifyNode(Node):\n",
+    "    def ports_setup(self):\n",
+    "        input_ports = {\n",
+    "            'df1': {\n",
+    "                PortsSpecSchema.port_type: [cudf.DataFrame, dask_cudf.DataFrame]\n",
+    "            },\n",
+    "            'df2': {\n",
+    "                PortsSpecSchema.port_type: [cudf.DataFrame, dask_cudf.DataFrame]\n",
+    "            }\n",
+    "        }\n",
+    "        output_ports = {\n",
+    "            'max_diff': {\n",
+    "                PortsSpecSchema.port_type: float\n",
+    "            }\n",
+    "        }\n",
+    "\n",
+    "        return NodePorts(inports=input_ports, outports=output_ports)\n",
+    "\n",
+    "    def columns_setup(self):\n",
+    "        pass\n",
+    "\n",
+    "    def process(self, inputs):\n",
+    "        df1 = inputs['df1']\n",
+    "        df2 = inputs['df2']\n",
+    "        col_df1 = self.conf['df1_col']\n",
+    "        col_df2 = self.conf['df2_col']\n",
+    "\n",
+    "        df1_col = df1[col_df1]\n",
+    "        if isinstance(df1, dask_cudf.DataFrame):\n",
+    "            # df1_col = df1_col.compute()\n",
+    "            pass\n",
+    "\n",
+    "        df2_col = df2[col_df2]\n",
+    "        if isinstance(df2, dask_cudf.DataFrame):\n",
+    "            # df2_col = df2_col.compute()\n",
+    "            pass\n",
+    "\n",
+    "        max_difference = (df1_col - df2_col).abs().max()\n",
+    "\n",
+    "        if isinstance(max_difference, dask.dataframe.core.Scalar):\n",
+    "            max_difference = float(max_difference.compute())\n",
+    "        \n",
+    "        # print('Max Difference: {}'.format(max_difference))\n",
+    "        # assert(max_difference < 1e-8)        \n",
+    "\n",
+    "        return {'max_diff': max_difference}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Max Difference cudf to numba: 2.220446049250313e-16\n",
+      "Max Difference cudf to cupy: 2.220446049250313e-16\n"
+     ]
+    }
+   ],
+   "source": [
+    "verify_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'verify_cudf_to_numba',\n",
+    "    TaskSpecSchema.node_type: VerifyNode,\n",
+    "    TaskSpecSchema.conf: {\n",
+    "        'df1_col': 'distance_cudf',\n",
+    "        'df2_col': 'distance_numba'\n",
+    "    },    \n",
+    "    TaskSpecSchema.inputs: {\n",
+    "        'df1': 'distance_by_cudf.distance_euclid_df',\n",
+    "        'df2': 'distance_by_numba.distance_df'\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "verify_tspec2 = {\n",
+    "    TaskSpecSchema.task_id: 'verify_cudf_to_cupy',\n",
+    "    TaskSpecSchema.node_type: VerifyNode,\n",
+    "    TaskSpecSchema.conf: {\n",
+    "        'df1_col': 'distance_cudf',\n",
+    "        'df2_col': 'distance_cupy'\n",
+    "    },    \n",
+    "    TaskSpecSchema.inputs: {\n",
+    "        'df1': 'distance_by_cudf.distance_euclid_df',\n",
+    "        'df2': 'distance_by_cupy.distance_df'\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "task_graph.extend([verify_tspec, verify_tspec2], replace=True)\n",
+    "task_graph.draw(show='ipynb', show_ports=True)\n",
+    "(max_cudf_to_numba_diff, max_cudf_to_cupy_diff) = task_graph.run([\n",
+    "    'verify_cudf_to_numba.max_diff',\n",
+    "    'verify_cudf_to_cupy.max_diff'\n",
+    "])\n",
+    "print('Max Difference cudf to numba: {}'.format(max_cudf_to_numba_diff))\n",
+    "print('Max Difference cudf to cupy: {}'.format(max_cudf_to_cupy_diff))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dask distributed computation\n",
+    "\n",
+    "Using Dask and `dask-cudf` we can run the Nodes with customized GPU kernels on distributed dataframes. Under the hood of the `Node` class the Dask delayed processing API is handled for cudf dataframes when the `self.delayed_process = True` flag is set.\n",
+    "\n",
+    "We first start a distributed Dask environment. When a dask client is instantiated it registers itself as the default Dask scheduler (<http://distributed.dask.org/en/latest/client.html>). Therefore all subsequent Dask distibuted dataframe operations will run in distributed fashion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/avolkov/progs/python_installs/miniconda3/envs/py36-rapids0.10/lib/python3.6/site-packages/distributed/dashboard/core.py:72: UserWarning: \n",
+      "Port 8787 is already in use. \n",
+      "Perhaps you already have a cluster running?\n",
+      "Hosting the diagnostics dashboard on a random port instead.\n",
+      "  warnings.warn(\"\\n\" + msg)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Client</h3>\n",
+       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:41691</li>\n",
+       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:45520/status' target='_blank'>http://127.0.0.1:45520/status</a>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
+       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Workers: </b>2</li>\n",
+       "  <li><b>Cores: </b>2</li>\n",
+       "  <li><b>Memory: </b>135.16 GB</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: 'tcp://127.0.0.1:41691' processes=2 threads=2, memory=135.16 GB>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dask_cuda import LocalCUDACluster\n",
+    "from dask.distributed import Client\n",
+    "\n",
+    "cluster = LocalCUDACluster()\n",
+    "client = Client(cluster)\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Dask status page can be displayed in a web browser at `<ip-address>:8787`. The ip-address corresponds to the machine where the dask cluster (scheduler) was launched. Most likely same ip-address as where this jupyter notebook is running. Using the Dask status page is convenient for monitoring dask distributed processing. <http://distributed.dask.org/en/latest/web.html>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next step is to partition the `cudf` dataframe into a `dask_cudf` dataframe. Here we make the number of partitions corresponding to the number of workers:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DistributedNode(Node):\n",
+    "\n",
+    "    def ports_setup(self):\n",
+    "        input_ports = {\n",
+    "            'points_df_in': {\n",
+    "                PortsSpecSchema.port_type: cudf.DataFrame\n",
+    "            }\n",
+    "        }\n",
+    "\n",
+    "        output_ports = {\n",
+    "            'points_ddf_out': {\n",
+    "                PortsSpecSchema.port_type: dask_cudf.DataFrame\n",
+    "            }\n",
+    "        }\n",
+    "\n",
+    "        return NodePorts(inports=input_ports, outports=output_ports)\n",
+    "\n",
+    "    def columns_setup(self,):\n",
+    "        required = {\n",
+    "            'x': 'float64',\n",
+    "            'y': 'float64'\n",
+    "        }\n",
+    "\n",
+    "        self.required = {\n",
+    "            'points_df_in': required,\n",
+    "            'points_ddf_out': required\n",
+    "        }\n",
+    "\n",
+    "    def process(self, inputs):\n",
+    "        npartitions = self.conf['npartitions']\n",
+    "        df = inputs['points_df_in']\n",
+    "        ddf = dask_cudf.from_cudf(df, npartitions=npartitions)\n",
+    "        return {'points_ddf_out': ddf}\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We add this distribution node to the computation graph to convert `cudf` dataframes into `dask-cudf` dataframes. The `dask-cudf` dataframes are handled automatically in gQuant when `self.delayed_process=True` within a `Node` implementation (setup in `columns_setup`). When using nodes with ports with `self.delayed_process=True` setting, it is required that all input and output ports be of type `cudf.DataFrame`. Otherwise don't set  `self.delayed_process` and one can write custom logic to handle distributed dataframes (refer to `VerifyNode` abover for an example where `dask_cudf` dataframes are handled directly within the process method)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "npartitions = len(client.scheduler_info()['workers'])\n",
+    "\n",
+    "\n",
+    "distribute_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'distributed_points',\n",
+    "    TaskSpecSchema.node_type: DistributedNode,\n",
+    "    TaskSpecSchema.conf: {'npartitions': npartitions},\n",
+    "    TaskSpecSchema.inputs: {\n",
+    "        'points_df_in': 'points_task.points_df_out'\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "dask_cudf_distance_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'distance_by_cudf',\n",
+    "    TaskSpecSchema.node_type: DistanceNode,\n",
+    "    TaskSpecSchema.conf: {},\n",
+    "    TaskSpecSchema.inputs: {\n",
+    "        'points_df_in': 'distributed_points.points_ddf_out'\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "dask_numba_distance_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'distance_by_numba',\n",
+    "    TaskSpecSchema.node_type: NumbaDistanceNode,\n",
+    "    TaskSpecSchema.conf: {},\n",
+    "    TaskSpecSchema.inputs: {\n",
+    "        'points_df_in': 'distributed_points.points_ddf_out'\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "dask_cupy_distance_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'distance_by_cupy',\n",
+    "    TaskSpecSchema.node_type: CupyDistanceNode,\n",
+    "    TaskSpecSchema.conf: {},\n",
+    "    TaskSpecSchema.inputs: {\n",
+    "        'points_df_in': 'distributed_points.points_ddf_out'\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "task_list = [\n",
+    "    points_tspec,\n",
+    "    distribute_tspec,\n",
+    "    dask_cudf_distance_tspec,\n",
+    "    dask_numba_distance_tspec,\n",
+    "    dask_cupy_distance_tspec\n",
+    "]\n",
+    "\n",
+    "task_graph = TaskGraph(task_list)\n",
+    "task_graph.draw(show='ipynb', show_ports=True)\n",
+    "\n",
+    "out_list = [\n",
+    "    'distributed_points.points_ddf_out',\n",
+    "    'distance_by_cudf.distance_euclid_df',\n",
+    "    'distance_by_numba.distance_df',\n",
+    "    'distance_by_cupy.distance_df'\n",
+    "]\n",
+    "(points_ddf, ddf_w_cudf, ddf_w_numba, ddf_w_cupy) = task_graph.run(out_list)\n",
+    "df_w_cudf = ddf_w_cudf.compute()\n",
+    "df_w_numba = ddf_w_numba.compute()\n",
+    "df_w_cupy = ddf_w_cupy.compute()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Verify the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HEAD points_ddf:\n",
+      "          x         y\n",
+      "0  0.887968  0.582714\n",
+      "1  0.146722  0.296758\n",
+      "2  0.391815  0.623228\n",
+      "3  0.882974  0.621067\n",
+      "4  0.794594  0.844349\n",
+      "\n",
+      "HEAD df_w_cudf:\n",
+      "          x         y  distance_cudf\n",
+      "0  0.887968  0.582714       1.062094\n",
+      "1  0.146722  0.296758       0.331048\n",
+      "2  0.391815  0.623228       0.736161\n",
+      "3  0.882974  0.621067       1.079522\n",
+      "4  0.794594  0.844349       1.159442\n",
+      "\n",
+      "HEAD df_w_numba:\n",
+      "          x         y  distance_numba\n",
+      "0  0.887968  0.582714        1.062094\n",
+      "1  0.146722  0.296758        0.331048\n",
+      "2  0.391815  0.623228        0.736161\n",
+      "3  0.882974  0.621067        1.079522\n",
+      "4  0.794594  0.844349        1.159442\n",
+      "\n",
+      "HEAD df_w_cupy:\n",
+      "          x         y  distance_cupy\n",
+      "0  0.887968  0.582714       1.062094\n",
+      "1  0.146722  0.296758       0.331048\n",
+      "2  0.391815  0.623228       0.736161\n",
+      "3  0.882974  0.621067       1.079522\n",
+      "4  0.794594  0.844349       1.159442\n",
+      "\n",
+      "Max Difference cudf to numba: 2.220446049250313e-16\n",
+      "Max Difference cudf to cupy: 2.220446049250313e-16\n"
+     ]
+    }
+   ],
+   "source": [
+    "verify_cudf_numba_tspec = verify_tspec.copy()\n",
+    "verify_cudf_cupy_tspec = verify_tspec2.copy()\n",
+    "\n",
+    "task_graph.extend(\n",
+    "    [verify_cudf_numba_tspec,\n",
+    "     verify_cudf_cupy_tspec],\n",
+    "    replace=True)\n",
+    "task_graph.draw(show='ipynb', show_ports=True)\n",
+    "\n",
+    "# Use results above and avoid re-running dask\n",
+    "replace_spec = {\n",
+    "    'distance_by_cudf': {\n",
+    "        TaskSpecSchema.load: {\n",
+    "            'distance_euclid_df': ddf_w_cudf\n",
+    "        }\n",
+    "    },\n",
+    "    'distance_by_numba': {\n",
+    "        TaskSpecSchema.load: {\n",
+    "            'distance_df': ddf_w_numba\n",
+    "        }\n",
+    "    },\n",
+    "    'distance_by_cupy': {\n",
+    "        TaskSpecSchema.load: {\n",
+    "            'distance_df': ddf_w_cupy\n",
+    "        }\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "(max_cudf_to_numba_diff, max_cudf_to_cupy_diff) = task_graph.run(\n",
+    "    ['verify_cudf_to_numba.max_diff',\n",
+    "     'verify_cudf_to_cupy.max_diff'],\n",
+    "    replace=replace_spec\n",
+    ")\n",
+    "\n",
+    "print('HEAD points_ddf:\\n{}\\n'.format(points_ddf.head()))\n",
+    "print('HEAD df_w_cudf:\\n{}\\n'.format(ddf_w_cudf.head()))\n",
+    "print('HEAD df_w_numba:\\n{}\\n'.format(ddf_w_numba.head()))\n",
+    "print('HEAD df_w_cupy:\\n{}\\n'.format(ddf_w_cupy.head()))\n",
+    "print('Max Difference cudf to numba: {}'.format(max_cudf_to_numba_diff))\n",
+    "print('Max Difference cudf to cupy: {}'.format(max_cudf_to_cupy_diff))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One limitation to be aware of when using customized kernels within Nodes in the Dask environment, is that each GPU kernel works on one partition of the dataframe. Therefore if the computation depends on other partitions of the dataframe the approach above does not work."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Saving Custom Nodes and Kernels\n",
+    "\n",
+    "The gQuant examples already implement a number of `Nodes`. These can be found in `gquant.plugin_nodes` submodules.\n",
+    "\n",
+    "The customized kernels and nodes can be saved to your own python modules for future re-use instead of having to re-define them at runtime. The nodes we defined above were to a written to a python module \"custom_port_nodes.py\" (the `DistanceNode` was simplified to ommit the absolute distance calculation). We will re-run our workflow importing the Nodes from the custom module we wrote out.\n",
+    "\n",
+    "When defining the tasks we specify `filepath` for the path to the python module that has the Node definition. Notice, that the `node_type` is specified as a string instead of class. The string is the class name of the node that will be imported for running a task."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "npartitions = len(client.scheduler_info()['workers'])\n",
+    "\n",
+    "points_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'points_task',\n",
+    "    TaskSpecSchema.node_type: 'PointNode',\n",
+    "    TaskSpecSchema.filepath: 'custom_port_nodes.py',\n",
+    "    TaskSpecSchema.conf: {'npts': 1000},\n",
+    "    TaskSpecSchema.inputs: {},\n",
+    "}\n",
+    "\n",
+    "distribute_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'distributed_points',\n",
+    "    TaskSpecSchema.node_type: 'DistributedNode',\n",
+    "    TaskSpecSchema.filepath: 'custom_port_nodes.py',\n",
+    "    TaskSpecSchema.conf: {'npartitions': npartitions},\n",
+    "    TaskSpecSchema.inputs: {\n",
+    "        'points_df_in': 'points_task.points_df_out'\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "dask_cudf_distance_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'distance_by_cudf',\n",
+    "    TaskSpecSchema.node_type: 'DistanceNode',\n",
+    "    TaskSpecSchema.filepath: 'custom_port_nodes.py',\n",
+    "    TaskSpecSchema.conf: {},\n",
+    "    TaskSpecSchema.inputs: {\n",
+    "        'points_df_in': 'distributed_points.points_ddf_out'\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "dask_numba_distance_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'distance_by_numba',\n",
+    "    TaskSpecSchema.node_type: 'NumbaDistanceNode',\n",
+    "    TaskSpecSchema.filepath: 'custom_port_nodes.py',\n",
+    "    TaskSpecSchema.conf: {},\n",
+    "    TaskSpecSchema.inputs: {\n",
+    "        'points_df_in': 'distributed_points.points_ddf_out'\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "dask_cupy_distance_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'distance_by_cupy',\n",
+    "    TaskSpecSchema.node_type: 'CupyDistanceNode',\n",
+    "    TaskSpecSchema.filepath: 'custom_port_nodes.py',\n",
+    "    TaskSpecSchema.conf: {},\n",
+    "    TaskSpecSchema.inputs: {\n",
+    "        'points_df_in': 'distributed_points.points_ddf_out'\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "verify_cudf_to_numba_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'verify_cudf_to_numba',\n",
+    "    TaskSpecSchema.node_type: 'VerifyNode',\n",
+    "    TaskSpecSchema.filepath: 'custom_port_nodes.py',\n",
+    "    TaskSpecSchema.conf: {\n",
+    "        'df1_col': 'distance_cudf',\n",
+    "        'df2_col': 'distance_numba'\n",
+    "    },    \n",
+    "    TaskSpecSchema.inputs: {\n",
+    "        'df1': 'distance_by_cudf.distance_df',\n",
+    "        'df2': 'distance_by_numba.distance_df'\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "verify_cudf_to_cupy_tspec = {\n",
+    "    TaskSpecSchema.task_id: 'verify_cudf_to_cupy',\n",
+    "    TaskSpecSchema.node_type: 'VerifyNode',\n",
+    "    TaskSpecSchema.filepath: 'custom_port_nodes.py',\n",
+    "    TaskSpecSchema.conf: {\n",
+    "        'df1_col': 'distance_cudf',\n",
+    "        'df2_col': 'distance_cupy'\n",
+    "    },    \n",
+    "    TaskSpecSchema.inputs: {\n",
+    "        'df1': 'distance_by_cudf.distance_df',\n",
+    "        'df2': 'distance_by_cupy.distance_df'\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "task_list = [\n",
+    "    points_tspec,\n",
+    "    distribute_tspec,\n",
+    "    dask_cudf_distance_tspec,\n",
+    "    dask_numba_distance_tspec,\n",
+    "    dask_cupy_distance_tspec,\n",
+    "    verify_cudf_to_numba_tspec,\n",
+    "    verify_cudf_to_cupy_tspec\n",
+    "]\n",
+    "\n",
+    "task_graph = TaskGraph(task_list)\n",
+    "task_graph.draw(show='ipynb', show_ports=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HEAD df_w_cudf:\n",
+      "          x         y  distance_cudf\n",
+      "0  0.868484  0.758179       1.152866\n",
+      "1  0.318385  0.046299       0.321734\n",
+      "2  0.844744  0.442833       0.953778\n",
+      "3  0.436758  0.348251       0.558602\n",
+      "4  0.197671  0.520553       0.556820\n",
+      "\n",
+      "HEAD df_w_numba:\n",
+      "          x         y  distance_numba\n",
+      "0  0.868484  0.758179        1.152866\n",
+      "1  0.318385  0.046299        0.321734\n",
+      "2  0.844744  0.442833        0.953778\n",
+      "3  0.436758  0.348251        0.558602\n",
+      "4  0.197671  0.520553        0.556820\n",
+      "\n",
+      "HEAD df_w_cupy:\n",
+      "          x         y  distance_cupy\n",
+      "0  0.868484  0.758179       1.152866\n",
+      "1  0.318385  0.046299       0.321734\n",
+      "2  0.844744  0.442833       0.953778\n",
+      "3  0.436758  0.348251       0.558602\n",
+      "4  0.197671  0.520553       0.556820\n",
+      "\n",
+      "Max Difference cudf to numba: 2.220446049250313e-16\n",
+      "Max Difference cudf to cupy: 2.220446049250313e-16\n"
+     ]
+    }
+   ],
+   "source": [
+    "out_list = [\n",
+    "    'distance_by_cudf.distance_df',\n",
+    "    'distance_by_numba.distance_df',\n",
+    "    'distance_by_cupy.distance_df',\n",
+    "    'verify_cudf_to_numba.max_diff',\n",
+    "    'verify_cudf_to_cupy.max_diff'\n",
+    "]\n",
+    "\n",
+    "(ddf_w_cudf, ddf_w_numba, ddf_w_cupy,\n",
+    " mdiff_cudf_to_numba, mdiff_cudf_to_cupy) = task_graph.run(out_list)\n",
+    "\n",
+    "print('HEAD df_w_cudf:\\n{}\\n'.format(ddf_w_cudf.head()))\n",
+    "print('HEAD df_w_numba:\\n{}\\n'.format(ddf_w_numba.head()))\n",
+    "print('HEAD df_w_cupy:\\n{}\\n'.format(ddf_w_cupy.head()))\n",
+    "print('Max Difference cudf to numba: {}'.format(mdiff_cudf_to_numba))\n",
+    "print('Max Difference cudf to cupy: {}'.format(mdiff_cudf_to_cupy))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final illustration is how to save and load a task graph to a file for re-use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "task_graph.save_taskgraph('custom_wflow.yaml')\n",
+    "task_graph = TaskGraph.load_taskgraph('custom_wflow.yaml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HEAD df_w_cudf:\n",
+      "          x         y  distance_cudf\n",
+      "0  0.431121  0.941755       1.035745\n",
+      "1  0.950709  0.448873       1.051349\n",
+      "2  0.224143  0.067438       0.234068\n",
+      "3  0.644774  0.583203       0.869401\n",
+      "4  0.223024  0.325308       0.394418\n",
+      "\n",
+      "HEAD df_w_numba:\n",
+      "          x         y  distance_numba\n",
+      "0  0.431121  0.941755        1.035745\n",
+      "1  0.950709  0.448873        1.051349\n",
+      "2  0.224143  0.067438        0.234068\n",
+      "3  0.644774  0.583203        0.869401\n",
+      "4  0.223024  0.325308        0.394418\n",
+      "\n",
+      "HEAD df_w_cupy:\n",
+      "          x         y  distance_cupy\n",
+      "0  0.431121  0.941755       1.035745\n",
+      "1  0.950709  0.448873       1.051349\n",
+      "2  0.224143  0.067438       0.234068\n",
+      "3  0.644774  0.583203       0.869401\n",
+      "4  0.223024  0.325308       0.394418\n",
+      "\n",
+      "Max Difference cudf to numba: 2.220446049250313e-16\n",
+      "Max Difference cudf to cupy: 2.220446049250313e-16\n"
+     ]
+    }
+   ],
+   "source": [
+    "# update npartitions in case the scheduler is running with\n",
+    "# different number of workers than what was saved.\n",
+    "npartitions = len(client.scheduler_info()['workers'])\n",
+    "replace_spec = {\n",
+    "    'distributed_points': {\n",
+    "        TaskSpecSchema.conf: {'npartitions': npartitions},\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "out_list = [\n",
+    "    'distance_by_cudf.distance_df',\n",
+    "    'distance_by_numba.distance_df',\n",
+    "    'distance_by_cupy.distance_df',\n",
+    "    'verify_cudf_to_numba.max_diff',\n",
+    "    'verify_cudf_to_cupy.max_diff'\n",
+    "]\n",
+    "\n",
+    "(ddf_w_cudf, ddf_w_numba, ddf_w_cupy,\n",
+    " mdiff_cudf_to_numba, mdiff_cudf_to_cupy) = task_graph.run(\n",
+    "    out_list, replace=replace_spec)\n",
+    "\n",
+    "print('HEAD df_w_cudf:\\n{}\\n'.format(ddf_w_cudf.head()))\n",
+    "print('HEAD df_w_numba:\\n{}\\n'.format(ddf_w_numba.head()))\n",
+    "print('HEAD df_w_cupy:\\n{}\\n'.format(ddf_w_cupy.head()))\n",
+    "print('Max Difference cudf to numba: {}'.format(mdiff_cudf_to_numba))\n",
+    "print('Max Difference cudf to cupy: {}'.format(mdiff_cudf_to_cupy))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Conclusion\n",
+    "\n",
+    "Using customized GPU kernels allows data scientists to implement and incorporate advanced algorithms. We demonstrated implementations using Numba and CuPy.\n",
+    "\n",
+    "The Numba approach enables data scientists to write GPU kernels directly in the Python language. Numba is easy to use for implementing and accelerating computations. However there is some overhead incurred for compiling the kernels whenever the Numba GPU kernels are used for the first time in a Python process. Currently Numba library only supports primitive data types. Some advanced CUDA programming features, such as function pointers and function recursions are not supported. \n",
+    "\n",
+    "The Cupy method is very flexible, because data scientists are writing C/C++ GPU kernels with CUDA directly. All the CUDA programming features are supported. CuPy compiles the kernel and caches the device code to the filesystem. The launch overhead is low. Also, the GPU kernel is built statically resulting in runtime efficiency. However it might be harder for data scientists to use, because C/C++ programming is more complicated. \n",
+    "\n",
+    "Below is a brief summary comparison table:\n",
+    "\n",
+    "| Methods | Development Difficulty | Flexibility | Efficiency | Latency |\n",
+    "|---|---|---|---|---|\n",
+    "| Numba method | medium | medium | low | high |\n",
+    "| CuPy method | hard | high  | high | low |\n",
+    "\n",
+    "We recommend that the data scientists select the approach appropriate for their task taking into consideration the efficiency, latency, difficulty and flexibility of their workflow. \n",
+    "\n",
+    "In this blog, we showed how to wrap the customized GPU kernels in gQuant nodes. Also, by taking advantage of having the gQuant handle the low-level Dask interfaces for the developer, we demonstrated how to use the gQuant workflow with Dask distributed computations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clean up\n",
+    "\n",
+    "# Shutdown the Dask cluster\n",
+    "client.close()\n",
+    "cluster.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py36-rapids0.10",
+   "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": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/custom_port_nodes.py b/notebooks/custom_port_nodes.py
new file mode 100644
index 00000000..9419d3e5
--- /dev/null
+++ b/notebooks/custom_port_nodes.py
@@ -0,0 +1,301 @@
+import math
+import numpy as np
+from numba import cuda
+import cupy
+import cudf
+import dask_cudf
+import dask
+
+from gquant.dataframe_flow import Node
+from gquant.dataframe_flow import NodePorts, PortsSpecSchema
+
+
+class PointNode(Node):
+
+    def ports_setup(self):
+        input_ports = {}
+        output_ports = {
+            'points_df_out': {
+                PortsSpecSchema.port_type: cudf.DataFrame
+            }
+        }
+
+        return NodePorts(inports=input_ports, outports=output_ports)
+
+    def columns_setup(self):
+        self.required = {}
+        self.addition = {
+           'points_df_out': {
+                'x': 'float64',
+                'y': 'float64'
+            }
+        }
+
+    def process(self, inputs):
+        npts = self.conf['npts']
+        df = cudf.DataFrame()
+        df['x'] = np.random.rand(npts)
+        df['y'] = np.random.rand(npts)
+
+        return {'points_df_out': df}
+
+
+class DistanceNode(Node):
+
+    def ports_setup(self):
+        input_ports = {
+            'points_df_in': {
+                PortsSpecSchema.port_type: cudf.DataFrame
+            }
+        }
+
+        output_ports = {
+            'distance_df': {
+                PortsSpecSchema.port_type: cudf.DataFrame
+            }
+        }
+
+        return NodePorts(inports=input_ports, outports=output_ports)
+
+    def columns_setup(self):
+        self.delayed_process = True
+
+        req_cols = {
+            'x': 'float64',
+            'y': 'float64'
+        }
+
+        self.required = {
+            'points_df_in': req_cols,
+            'distance_df': req_cols
+        }
+
+        self.addition = {
+            'distance_df': {
+                'distance_cudf': 'float64'
+            }
+        }
+
+    def process(self, inputs):
+        df = inputs['points_df_in']
+
+        # DEBUGGING
+        # try:
+        #     from dask.distributed import get_worker
+        #     worker = get_worker()
+        #     print('worker{} process NODE "{}" worker: {}'.format(
+        #         worker.name, self.uid, worker))
+        # except (ValueError, ImportError):
+        #     pass
+
+        df['distance_cudf'] = (df['x'] ** 2 + df['y'] ** 2).sqrt()
+
+        return {'distance_df': df}
+
+
+@cuda.jit
+def distance_kernel(x, y, distance, array_len):
+    # ii - overall thread index
+    ii = cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x
+    if ii < array_len:
+        distance[ii] = math.sqrt(x[ii] ** 2 + y[ii] ** 2)
+
+
+class NumbaDistanceNode(Node):
+
+    def ports_setup(self):
+        input_ports = {
+            'points_df_in': {
+                PortsSpecSchema.port_type: cudf.DataFrame
+            }
+        }
+
+        output_ports = {
+            'distance_df': {
+                PortsSpecSchema.port_type: cudf.DataFrame
+            }
+        }
+
+        return NodePorts(inports=input_ports, outports=output_ports)
+
+    def columns_setup(self,):
+        self.delayed_process = True
+
+        required = {'x': 'float64',
+                    'y': 'float64'}
+        self.required = {
+            'points_df_in': required,
+            'distance_df': required
+        }
+        self.addition = {
+            'distance_df': {'distance_numba': 'float64'}
+        }
+
+    def process(self, inputs):
+        df = inputs['points_df_in']
+
+        # DEBUGGING
+        # try:
+        #     from dask.distributed import get_worker
+        #     worker = get_worker()
+        #     print('worker{} process NODE "{}" worker: {}'.format(
+        #         worker.name, self.uid, worker))
+        # except (ValueError, ImportError):
+        #     pass
+
+        number_of_threads = 16
+        number_of_blocks = ((len(df) - 1) // number_of_threads) + 1
+        # Inits device array by setting 0 for each index.
+        # df['distance_numba'] = 0.0
+        darr = cuda.device_array(len(df))
+        distance_kernel[(number_of_blocks,), (number_of_threads,)](
+            df['x'].to_gpu_array(),
+            df['y'].to_gpu_array(),
+            darr,
+            len(df))
+        df['distance_numba'] = darr
+        return {'distance_df': df}
+
+
+raw_kernel = cupy.RawKernel(r'''
+    extern "C" __global__
+    void compute_distance(const double* x, const double* y,
+            double* distance, int arr_len) {
+        int tid = blockDim.x * blockIdx.x + threadIdx.x;
+        if (tid < arr_len){
+        distance[tid] = sqrt(x[tid]*x[tid] + y[tid]*y[tid]);
+        }
+    }
+''', 'compute_distance')
+
+
+class CupyDistanceNode(Node):
+
+    def ports_setup(self):
+        input_ports = {
+            'points_df_in': {
+                PortsSpecSchema.port_type: cudf.DataFrame
+            }
+        }
+
+        output_ports = {
+            'distance_df': {
+                PortsSpecSchema.port_type: cudf.DataFrame
+            }
+        }
+
+        return NodePorts(inports=input_ports, outports=output_ports)
+
+    def columns_setup(self,):
+        cols_required = {'x': 'float64',
+                         'y': 'float64'}
+        self.required = {
+            'points_df_in': cols_required,
+            'distance_df': cols_required
+        }
+
+        self.addition = {
+            'distance_df': {
+                'distance_cupy': 'float64'
+            }
+        }
+        self.delayed_process = True
+
+    def process(self, inputs):
+        df = inputs['points_df_in']
+        cupy_x = cupy.asarray(df['x'])
+        cupy_y = cupy.asarray(df['y'])
+        number_of_threads = 16
+        number_of_blocks = (len(df) - 1) // number_of_threads + 1
+        dis = cupy.ndarray(len(df), dtype=cupy.float64)
+        raw_kernel((number_of_blocks,), (number_of_threads,),
+                   (cupy_x, cupy_y, dis, len(df)))
+        df['distance_cupy'] = dis
+
+        return {'distance_df': df}
+
+
+class DistributedNode(Node):
+
+    def ports_setup(self):
+        input_ports = {
+            'points_df_in': {
+                PortsSpecSchema.port_type: cudf.DataFrame
+            }
+        }
+
+        output_ports = {
+            'points_ddf_out': {
+                PortsSpecSchema.port_type: dask_cudf.DataFrame
+            }
+        }
+
+        return NodePorts(inports=input_ports, outports=output_ports)
+
+    def columns_setup(self,):
+        required = {
+            'x': 'float64',
+            'y': 'float64'
+        }
+
+        self.required = {
+            'points_df_in': required,
+            'points_ddf_out': required
+        }
+
+    def process(self, inputs):
+        npartitions = self.conf['npartitions']
+        df = inputs['points_df_in']
+        ddf = dask_cudf.from_cudf(df, npartitions=npartitions)
+        return {'points_ddf_out': ddf}
+
+
+class VerifyNode(Node):
+
+    def ports_setup(self):
+        input_ports = {
+            'df1': {
+                PortsSpecSchema.port_type: [cudf.DataFrame,
+                                            dask_cudf.DataFrame]
+            },
+            'df2': {
+                PortsSpecSchema.port_type: [cudf.DataFrame,
+                                            dask_cudf.DataFrame]
+            }
+        }
+        output_ports = {
+            'max_diff': {
+                PortsSpecSchema.port_type: float
+            }
+        }
+
+        return NodePorts(inports=input_ports, outports=output_ports)
+
+    def columns_setup(self):
+        pass
+
+    def process(self, inputs):
+        df1 = inputs['df1']
+        df2 = inputs['df2']
+        col_df1 = self.conf['df1_col']
+        col_df2 = self.conf['df2_col']
+
+        df1_col = df1[col_df1]
+        if isinstance(df1, dask_cudf.DataFrame):
+            # df1_col = df1_col.compute()
+            pass
+
+        df2_col = df2[col_df2]
+        if isinstance(df2, dask_cudf.DataFrame):
+            # df2_col = df2_col.compute()
+            pass
+
+        max_difference = (df1_col - df2_col).abs().max()
+
+        if isinstance(max_difference, dask.dataframe.core.Scalar):
+            max_difference = float(max_difference.compute())
+
+        # print('Max Difference: {}'.format(max_difference))
+        # assert(max_difference < 1e-8)
+
+        return {'max_diff': max_difference}
diff --git a/tests/unit/custom_port_nodes.py b/tests/unit/custom_port_nodes.py
new file mode 100644
index 00000000..644c2920
--- /dev/null
+++ b/tests/unit/custom_port_nodes.py
@@ -0,0 +1,109 @@
+import numpy as np
+import cudf
+
+from gquant.dataframe_flow import Node
+from gquant.dataframe_flow import NodePorts, PortsSpecSchema
+
+
+class PointNoPortsNode(Node):
+
+    def columns_setup(self):
+        self.required = {}
+        self.addition = {
+            'x': 'float64',
+            'y': 'float64'
+        }
+
+    def process(self, inputs):
+        npts = self.conf['npts']
+        df = cudf.DataFrame()
+        df['x'] = np.random.rand(npts)
+        df['y'] = np.random.rand(npts)
+
+        return df
+
+
+class PointNode(Node):
+
+    def ports_setup(self):
+        input_ports = {}
+        output_ports = {
+            'points_df_out': {
+                PortsSpecSchema.port_type: cudf.DataFrame
+            }
+        }
+
+        return NodePorts(inports=input_ports, outports=output_ports)
+
+    def columns_setup(self):
+        self.required = {}
+        self.addition = {
+           'points_df_out': {
+                'x': 'float64',
+                'y': 'float64'
+            }
+        }
+
+    def process(self, inputs):
+        npts = self.conf['npts']
+        seed = self.conf.get('nseed')
+        if seed is not None:
+            np.random.seed(seed)
+        df = cudf.DataFrame()
+        df['x'] = np.random.rand(npts)
+        df['y'] = np.random.rand(npts)
+
+        return {'points_df_out': df}
+
+
+class DistanceNode(Node):
+
+    def ports_setup(self):
+        input_ports = {
+            'points_df_in': {
+                PortsSpecSchema.port_type: cudf.DataFrame
+            }
+        }
+
+        output_ports = {
+            'distance_df': {
+                PortsSpecSchema.port_type: cudf.DataFrame
+            }
+        }
+
+        return NodePorts(inports=input_ports, outports=output_ports)
+
+    def columns_setup(self):
+        self.delayed_process = True
+
+        req_cols = {
+            'x': 'float64',
+            'y': 'float64'
+        }
+
+        self.required = {
+            'points_df_in': req_cols,
+            'distance_df': req_cols
+        }
+
+        self.addition = {
+            'distance_df': {
+                'distance_cudf': 'float64'
+            }
+        }
+
+    def process(self, inputs):
+        df = inputs['points_df_in']
+
+        # DEBUGGING
+        # try:
+        #     from dask.distributed import get_worker
+        #     worker = get_worker()
+        #     print('worker{} process NODE "{}" worker: {}'.format(
+        #         worker.name, self.uid, worker))
+        # except (ValueError, ImportError):
+        #     pass
+
+        df['distance_cudf'] = (df['x'] ** 2 + df['y'] ** 2).sqrt()
+
+        return {'distance_df': df}
diff --git a/tests/unit/test_node_api.py b/tests/unit/test_node_api.py
new file mode 100644
index 00000000..4858cdce
--- /dev/null
+++ b/tests/unit/test_node_api.py
@@ -0,0 +1,135 @@
+'''
+gQuant Node API Unit Tests
+
+To run unittests:
+
+# Using standard library unittest
+
+python -m unittest -v
+python -m unittest tests/unit/test_node_api.py -v
+
+or
+
+python -m unittest discover <test_directory>
+python -m unittest discover -s <directory> -p 'test_*.py'
+
+# Using pytest
+# "conda install pytest" or "pip install pytest"
+pytest -v tests
+pytest -v tests/unit/test_node_api.py
+
+'''
+import os
+import unittest
+
+from gquant.dataframe_flow import TaskSpecSchema
+from gquant.dataframe_flow.task import Task
+from gquant.dataframe_flow._node import _Node
+from gquant.dataframe_flow.node import (Node, _PortsMixin)
+from gquant.dataframe_flow._node_flow import NodeTaskGraphMixin
+
+from .utils import make_orderer
+
+ordered, compare = make_orderer()
+unittest.defaultTestLoader.sortTestMethodsUsing = compare
+
+
+class TestNodeAPI(unittest.TestCase):
+
+    def setUp(self):
+        custom_module = '{}/custom_port_nodes.py'.format(
+            os.path.dirname(os.path.realpath(__file__)))
+
+        points_task_spec = {
+            TaskSpecSchema.task_id: 'points_task',
+            TaskSpecSchema.node_type: 'PointNode',
+            TaskSpecSchema.filepath: custom_module,
+            TaskSpecSchema.conf: {'npts': 1000},
+            TaskSpecSchema.inputs: []
+        }
+
+        self.points_task = Task(points_task_spec)
+
+        distance_task_spec = {
+            TaskSpecSchema.task_id: 'distance_by_cudf',
+            TaskSpecSchema.node_type: 'DistanceNode',
+            TaskSpecSchema.filepath: custom_module,
+            TaskSpecSchema.conf: {},
+            TaskSpecSchema.inputs: {
+                'points_df_in': 'points_task.points_df_out'
+            }
+        }
+
+        self.distance_task = Task(distance_task_spec)
+
+        points_noports_task_spec = {
+            TaskSpecSchema.task_id: 'points_noport_task',
+            TaskSpecSchema.node_type: 'PointNoPortsNode',
+            TaskSpecSchema.filepath: custom_module,
+            TaskSpecSchema.conf: {'npts': 1000},
+            TaskSpecSchema.inputs: []
+        }
+
+        self.points_noports_task = Task(points_noports_task_spec)
+
+    def tearDown(self):
+        pass
+
+    @ordered
+    def test_node_instantiation(self):
+        '''Test node instantiation.
+
+            1. Test that you cannot instantiate an abstract base class without
+            first implementing the methods requiring override.
+
+            2. Check for the base and base mixin classes in a Node class
+            implementation.
+        '''
+        points_task = self.points_task
+
+        # assert cannot instantiate Node without overriding columns_setup
+        # and process
+        with self.assertRaises(TypeError) as cm:
+            _ = Node(points_task)
+        err_msg = '{}'.format(cm.exception)
+        self.assertEqual(
+            err_msg,
+            "Can't instantiate abstract class Node with abstract methods "
+            "columns_setup, process")
+
+        points_node = points_task.get_node_obj()
+
+        self.assertIsInstance(points_node, _Node)
+        self.assertIsInstance(points_node, Node)
+        self.assertIsInstance(points_node, _PortsMixin)
+        self.assertNotIsInstance(points_node, NodeTaskGraphMixin)
+
+        points_node = points_task.get_node_obj(tgraph_mixin=True)
+        self.assertIsInstance(points_node, NodeTaskGraphMixin)
+
+    @ordered
+    def test_node_ports(self):
+        '''Test the ports related APIs such as existence of ports, input ports,
+        and output ports.
+        '''
+
+        points_node = self.points_task.get_node_obj()
+        self.assertTrue(points_node._using_ports())
+
+        points_noport_node = self.points_noports_task.get_node_obj()
+        self.assertFalse(points_noport_node._using_ports())
+        with self.assertRaises(NotImplementedError):
+            _ = points_noport_node._get_input_ports()
+        with self.assertRaises(NotImplementedError):
+            _ = points_noport_node._get_output_ports()
+
+        distance_node = self.distance_task.get_node_obj()
+        iports = distance_node._get_input_ports()
+        oports = distance_node._get_output_ports()
+
+        self.assertEqual(iports, ['points_df_in'])
+        self.assertEqual(oports, ['distance_df'])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/unit/test_taskgraph_api.py b/tests/unit/test_taskgraph_api.py
new file mode 100644
index 00000000..d612b74e
--- /dev/null
+++ b/tests/unit/test_taskgraph_api.py
@@ -0,0 +1,281 @@
+'''
+gQuant TaskGraph API Unit Tests
+
+To run unittests:
+
+# Using standard library unittest
+
+python -m unittest -v
+python -m unittest tests/unit/test_taskgraph_api.py -v
+
+or
+
+python -m unittest discover <test_directory>
+python -m unittest discover -s <directory> -p 'test_*.py'
+
+# Using pytest
+# "conda install pytest" or "pip install pytest"
+pytest -v tests
+pytest -v tests/unit/test_taskgraph_api.py
+
+'''
+import os
+import shutil
+import tempfile
+from difflib import context_diff
+import yaml
+from io import StringIO
+import warnings
+import unittest
+
+from gquant.dataframe_flow import (TaskSpecSchema, TaskGraph)
+from gquant.dataframe_flow.task import DEFAULT_MODULE  # noqa: F401
+from gquant.dataframe_flow import Node
+
+from .utils import make_orderer
+
+ordered, compare = make_orderer()
+unittest.defaultTestLoader.sortTestMethodsUsing = compare
+
+
+TASKGRAPH_YAML = \
+    '''- id: points_task
+  type: PointNode
+  conf:
+    npts: 1000
+  inputs: []
+- id: distance_by_cudf
+  type: DistanceNode
+  conf: {}
+  inputs:
+    points_df_in: points_task.points_df_out
+'''
+
+
+class TestTaskGraphAPI(unittest.TestCase):
+    def setUp(self):
+        import gc  # python garbage collector
+        import cudf
+
+        # warmup
+        s = cudf.Series([1, 2, 3, None, 4])
+        del(s)
+        gc.collect()
+
+        os.environ['GQUANT_PLUGIN_MODULE'] = 'tests.unit.custom_port_nodes'
+
+        points_task_spec = {
+            TaskSpecSchema.task_id: 'points_task',
+            TaskSpecSchema.node_type: 'PointNode',
+            TaskSpecSchema.conf: {'npts': 1000},
+            TaskSpecSchema.inputs: []
+        }
+
+        distance_task_spec = {
+            TaskSpecSchema.task_id: 'distance_by_cudf',
+            TaskSpecSchema.node_type: 'DistanceNode',
+            TaskSpecSchema.conf: {},
+            TaskSpecSchema.inputs: {
+                'points_df_in': 'points_task.points_df_out'
+            }
+        }
+
+        tspec_list = [points_task_spec, distance_task_spec]
+
+        self.tgraph = TaskGraph(tspec_list)
+
+        # Create a temporary directory
+        self._test_dir = tempfile.mkdtemp()
+        os.environ['GQUANT_CACHE_DIR'] = os.path.join(self._test_dir, '.cache')
+
+    def tearDown(self):
+        global DEFAULT_MODULE
+        os.environ['GQUANT_PLUGIN_MODULE'] = DEFAULT_MODULE
+        os.environ['GQUANT_CACHE_DIR'] = Node.cache_dir
+        shutil.rmtree(self._test_dir)
+
+    @ordered
+    def test_viz_graph(self):
+        '''Test taskgraph to networkx graph conversion for graph visualization.
+        '''
+        nx_graph = self.tgraph.viz_graph(show_ports=True)
+        nx_nodes = [
+            'points_task', 'points_task.points_df_out',
+            'distance_by_cudf', 'distance_by_cudf.distance_df'
+        ]
+        nx_edges = [
+            ('points_task', 'points_task.points_df_out'),
+            ('points_task.points_df_out', 'distance_by_cudf'),
+            ('distance_by_cudf', 'distance_by_cudf.distance_df')
+        ]
+        self.assertEqual(list(nx_graph.nodes), nx_nodes)
+        self.assertEqual(list(nx_graph.edges), nx_edges)
+
+    @ordered
+    def test_build(self):
+        '''Test build of a taskgraph and that all inputs and outputs are set
+        for the tasks withink a taskgraph.
+        '''
+        self.tgraph.build()
+
+        points_node = self.tgraph['points_task']
+        distance_node = self.tgraph['distance_by_cudf']
+
+        onode_info = {
+            'to_node': distance_node,
+            'to_port': 'points_df_in',
+            'from_port': 'points_df_out'
+        }
+        self.assertIn(onode_info, points_node.outputs)
+
+        onode_cols = {
+            'points_df_out': {
+                'x': 'float64',
+                'y': 'float64'
+            }
+        }
+
+        self.assertEqual(onode_cols, points_node.output_columns)
+
+        inode_info = {
+            'from_node': points_node,
+            'from_port': 'points_df_out',
+            'to_port': 'points_df_in'
+        }
+        self.assertIn(inode_info, distance_node.inputs)
+
+        inode_in_cols = {
+            'points_df_in': {
+                'x': 'float64',
+                'y': 'float64'
+            }
+        }
+        self.assertEqual(inode_in_cols, distance_node.input_columns)
+
+        inode_out_cols = {
+            'distance_df': {
+                'x': 'float64',
+                'y': 'float64',
+                'distance_cudf': 'float64'
+            }
+        }
+        self.assertEqual(inode_out_cols, distance_node.output_columns)
+
+    @ordered
+    def test_run(self):
+        '''Test that a taskgraph can run successfully.
+        '''
+        outlist = ['distance_by_cudf.distance_df']
+        # Using numpy random seed to get repeatable and deterministic results.
+        # For seed 2335 should get something around 761.062831178.
+        replace_spec = {
+            'points_task': {
+                TaskSpecSchema.conf: {
+                    'npts': 1000,
+                    'nseed': 2335
+                }
+            }
+        }
+        (dist_df_w_cudf, ) = self.tgraph.run(
+            outputs=outlist, replace=replace_spec)
+        dist_sum = dist_df_w_cudf['distance_cudf'].sum()
+        # self.assertAlmostEqual(dist_sum, 0.0, places, msg, delta)
+        self.assertAlmostEqual(dist_sum, 761.062831178)  # match to 7 places
+
+    @ordered
+    def test_save(self):
+        '''Test that a taskgraph can be save to a yaml file.
+        '''
+        workflow_file = os.path.join(self._test_dir,
+                                     'test_save_taskgraph.yaml')
+        self.tgraph.save_taskgraph(workflow_file)
+
+        with open(workflow_file) as wf:
+            workflow_str = wf.read()
+
+        # verify the workflow contentst same as expected. Empty list if same.
+        global TASKGRAPH_YAML
+        cdiff = list(context_diff(TASKGRAPH_YAML, workflow_str))
+        cdiff_empty = cdiff == []
+
+        err_msg = 'Taskgraph yaml contents do not match expected results.\n'\
+            'SHOULD HAVE SAVED:\n\n'\
+            '{wyaml}\n\n'\
+            'INSTEAD FILE CONTAINS:\n\n'\
+            '{fcont}\n\n'\
+            'DIFF:\n\n'\
+            '{diff}'.format(wyaml=TASKGRAPH_YAML, fcont=workflow_str,
+                            diff=''.join(cdiff))
+
+        self.assertTrue(cdiff_empty, err_msg)
+
+    @ordered
+    def test_load(self):
+        '''Test that a taskgraph can be loaded from a yaml file.
+        '''
+        workflow_file = os.path.join(self._test_dir,
+                                     'test_load_taskgraph.yaml')
+
+        global TASKGRAPH_YAML
+        with open(workflow_file, 'w') as wf:
+            wf.write(TASKGRAPH_YAML)
+
+        tspec_list = [task._task_spec for task in self.tgraph]
+
+        tgraph = TaskGraph.load_taskgraph(workflow_file)
+        all_tasks_exist = True
+        for task in tgraph:
+            if task._task_spec not in tspec_list:
+                all_tasks_exist = False
+                break
+
+        with StringIO() as yf:
+            yaml.dump(tspec_list, yf,
+                      default_flow_style=False, sort_keys=False)
+            yf.seek(0)
+
+            err_msg = 'Load taskgraph failed. Missing expected task items.\n'\
+                'EXPECTED TASKGRAPH YAML:\n\n'\
+                '{wyaml}\n\n'\
+                'GOT TASKS FORMATTED AS YAML:\n\n'\
+                '{tlist}\n\n'.format(wyaml=TASKGRAPH_YAML, tlist=yf.read())
+
+            self.assertTrue(all_tasks_exist, err_msg)
+
+    @ordered
+    def test_save_load_cache(self):
+        '''Test caching of tasks outputs within a taskgraph.
+
+            1. Save points_task output to cache when running the taskgraph.
+            2. Load points_task df from cache when running the taskgraph.
+        '''
+        replace_spec = {'points_task': {TaskSpecSchema.save: True}}
+        outlist = ['distance_by_cudf.distance_df']
+
+        with warnings.catch_warnings():
+            # ignore UserWarning: Using CPU via Pandas to write HDF dataset
+            warnings.filterwarnings(
+                'ignore',
+                message='Using CPU via Pandas to write HDF dataset',
+                category=UserWarning,)
+            # ignore RuntimeWarning: numpy.ufunc size changed
+            warnings.filterwarnings('ignore',
+                                    category=RuntimeWarning,
+                                    message='numpy.ufunc size changed')
+            (_, ) = self.tgraph.run(outputs=outlist, replace=replace_spec)
+
+        cache_dir = os.path.join(self._test_dir, '.cache', 'points_task.hdf5')
+        self.assertTrue(os.path.exists(cache_dir))
+
+        replace_spec = {'points_task': {TaskSpecSchema.load: True}}
+        with warnings.catch_warnings():
+            # ignore UserWarning: Using CPU via Pandas to read HDF dataset
+            warnings.filterwarnings(
+                'ignore',
+                message='Using CPU via Pandas to read HDF dataset',
+                category=UserWarning)
+            (_, ) = self.tgraph.run(outputs=outlist, replace=replace_spec)
+
+
+if __name__ == '__main__':
+    unittest.main()

From 4165d793d79fdbabe8f11875a16bfc92cf4d7c96 Mon Sep 17 00:00:00 2001
From: yidong72 <43824965+yidong72@users.noreply.github.com>
Date: Thu, 13 Feb 2020 13:52:44 -0500
Subject: [PATCH 3/6] [REVIEW] upgrade to RAPIDS 0.11 (#76)

- update to RAPIDS 0.11
- fix the unit test errors
- remove the steps of converting to pandas
- remove pandas conversion
- add the profiler run
- add the notebook change
- fix the cuda version
---
 docker/build.sh                               |  92 ++--
 gquant/dataframe_flow/_node_flow.py           |  24 +-
 gquant/dataframe_flow/node.py                 |   1 +
 gquant/dataframe_flow/task.py                 |   6 +-
 gquant/dataframe_flow/taskGraph.py            |  11 +-
 .../strategy/xgboostStrategyNode.py           |   9 +-
 notebooks/01_tutorial.ipynb                   | 323 +++++++------
 notebooks/02_single_stock_trade.ipynb         |   3 +-
 notebooks/03_simple_dask_example.ipynb        | 453 +++++++++++++++++-
 notebooks/04_portfolio_trade.ipynb            | 122 +++--
 notebooks/05_customize_nodes.ipynb            |   3 +-
 .../05b_customize_nodes_with_ports.ipynb      |   8 +-
 notebooks/06_xgboost_trade.ipynb              | 102 ++--
 notebooks/07_fractional_differencing.ipynb    |   3 +-
 setup.py                                      |   2 +-
 15 files changed, 876 insertions(+), 286 deletions(-)

diff --git a/docker/build.sh b/docker/build.sh
index c3f1fff2..0b1cab85 100644
--- a/docker/build.sh
+++ b/docker/build.sh
@@ -2,47 +2,60 @@
 
 echo "Building gQuant container..."
 
-echo -e "Please, select the option which better fits your system configuration:\n" \
-        " - '1' for Ubuntu 16.04 + cuda 9.2\n" \
-        " - '2' for Ubuntu 16.04 + cuda 10.0\n" \
-        " - '3' for Ubuntu 18.04 + cuda 9.2\n" \
-        " - '4' for Ubuntu 18.04 + cuda 10.0"
+echo -e "\nPlease, select your operating system:\n" \
+     " - '1' for Ubuntu 16.04\n" \
+     " - '2' for Ubuntu 18.04\n" \
+     " - '3' for CentOS"
 
-read -p "Enter your option and hit return [1]-4: " SYSTEM_CONFIGURATION
+read -p "Enter your option and hit return [1]-3: " OPERATING_SYSTEM
 
-SYSTEM_CONFIGURATION=${SYSTEM_CONFIGURATION:-1}
-case $SYSTEM_CONFIGURATION in
+OPERATING_SYSTEM=${OPERATING_SYSTEM:-1}
+case $OPERATING_SYSTEM in
     2)
-        echo "Ubuntu 16.04 + cuda 10.0 selected."
-        OS_STR='16.04'
-        CONTAINER_VER='10.0'
-        CUPY='cupy-cuda100'
-        ;;
+	echo "Ubuntu 18.04 selected."
+	OS_STR="ubuntu18.04"
+	;;
     3)
-        echo "Ubuntu 18.04 + cuda 9.2 selected."
-        OS_STR='18.04'
-        CONTAINER_VER='9.2'
-        CUPY='cupy-cuda92'
-        ;;
-    4)
-        echo "Ubuntu 18.04 + cuda 10.0 selected."
-        OS_STR='18.04'
+	echo "CentOS selected."
+	OS_STR="centos7"
+	;;
+    *)
+	echo "Ubuntu 16.04 selected."
+	OS_STR="ubuntu16.04"
+	;;
+esac
+
+echo -e "\nPlease, select your cuda version:\n" \
+     " - '1' for cuda 9.2\n" \
+     " - '2' for cuda 10.0\n" \
+     " - '3' for cuda 10.1.2"
+
+read -p "Enter your option and hit return [1]-3: " CUDA_VERSION
+
+RAPIDS_VERSION="0.11"
+
+CUDA_VERSION=${CUDA_VERSION:-1}
+case $CUDA_VERSION in
+    2)
+	echo "cuda 10.0 selected."
 	CONTAINER_VER='10.0'
 	CUPY='cupy-cuda100'
-        ;;
+	;;
+    3)
+	echo "cuda 10.1.2 selected."
+	CONTAINER_VER='10.1'
+	CUPY='cupy-cuda101'
+	;;
     *)
-        echo "Ubuntu 16.04 + cuda 9.2 selected."
-        OS_STR='16.04'
-        CONTAINER_VER='9.2'
-        CUPY='cupy-cuda92'
-        ;;
+	echo "cuda 9.2 selected."
+	CONTAINER_VER='9.2'
+	CUPY='cupy-cuda92'
+	;;
 esac
 
-CONTAINER="nvcr.io/nvidia/rapidsai/rapidsai:0.10-cuda${CONTAINER_VER}-runtime-ubuntu${OS_STR}"
-
+CONTAINER="nvcr.io/nvidia/rapidsai/rapidsai:${RAPIDS_VERSION}-cuda${CONTAINER_VER}-runtime-${OS_STR}"
 
 D_FILE=${D_FILE:='Dockerfile.Rapids'}
-D_CONT=${D_CONT:='gquant/gquant:latest'}
 
 mkdir -p gQuant
 cp -r ../gquant ./gQuant
@@ -52,16 +65,20 @@ cp ../setup.py ./gQuant
 cp ../LICENSE ./gQuant
 rsync -av --progress ../notebooks ./gQuant --exclude data --exclude .cache --exclude many-small --exclude storage --exclude dask-worker-space --exclude __pycache__
 
+gquant_ver=$(grep version gQuant/setup.py | sed "s/^.*version='\([^;]*\)'.*/\1/")
+D_CONT=${D_CONT:="gquant/gquant:${gquant_ver}_${OS_STR}_${CONTAINER_VER}_${RAPIDS_VERSION}"}
+
 cat > $D_FILE <<EOF
 FROM $CONTAINER
 USER root
-
 ADD ./gQuant /rapids/gQuant
-
-RUN apt-get update && apt-get install -y libfontconfig1 libxrender1
-
+RUN if [ "$OS_STR" = "centos7" ]; then \
+        yum install -y fontconfig-devel libXrender ; \
+    else \
+        apt-get update && apt-get install -y libfontconfig1 libxrender1 ; \
+    fi
+# RUN apt-get update && apt-get install -y libfontconfig1 libxrender1
 SHELL ["bash","-c"]
-
 #
 # Additional python libs
 #
@@ -71,12 +88,10 @@ RUN source activate rapids \
 RUN source activate rapids \
     && cd /rapids/gQuant \
     && pip install .
-
 RUN source activate rapids \ 
     && conda install -y -c conda-forge dask-labextension recommonmark numpydoc sphinx_rtd_theme pudb \
     python-graphviz bqplot=0.11.5 nodejs=11.11.0 jupyterlab=0.35.4 ipywidgets=7.4.2 pytables mkl numexpr \
     pydot
-
 #
 # required set up
 #
@@ -85,14 +100,11 @@ RUN source activate rapids \
     && jupyter labextension install bqplot@0.4.5 --no-build \
     && mkdir /.local /.jupyter /.config /.cupy  \
     && chmod 777 /.local /.jupyter /.config /.cupy
-
 RUN source activate rapids \ 
     && jupyter lab build && jupyter lab clean
-
 EXPOSE 8888
 EXPOSE 8787
 EXPOSE 8786
-
 WORKDIR /rapids
 EOF
 
diff --git a/gquant/dataframe_flow/_node_flow.py b/gquant/dataframe_flow/_node_flow.py
index 310cd245..bb057cfd 100644
--- a/gquant/dataframe_flow/_node_flow.py
+++ b/gquant/dataframe_flow/_node_flow.py
@@ -654,7 +654,7 @@ def get_pout(out_dict, port):
         # https://docs.dask.org/en/latest/dataframe-api.html#dask.dataframe.DataFrame.persist
         # Otherwise process will run several times.
         for inputs_ in inputs_dly.values():
-            output_df_dly = dask.delayed(self.process)(inputs_)
+            output_df_dly = dask.delayed(self.decorate_process())(inputs_)
             output_df_dly_per = output_df_dly.persist()
             for oport in self._get_output_ports():
                 oport_out = dask.delayed(get_pout)(
@@ -672,6 +672,20 @@ def get_pout(out_dict, port):
 
         return output_df
 
+    def decorate_process(self):
+        import time
+        def timer(*argv):
+            start = time.time()
+            result = self.process(*argv)
+            end = time.time()
+            print('id:%s process time:%.3fs' % (self.uid, end-start))
+            return result
+        if self.profile:
+            return timer
+        else:
+            return self.process
+
+
     def __call__(self, inputs_data):
         if self._using_ports():
             # nodes with ports take dictionary as inputs
@@ -689,14 +703,14 @@ def __call__(self, inputs_data):
                 output_df = self.load
         else:
             if not self.delayed_process:
-                output_df = self.process(inputs)
+                output_df = self.decorate_process()(inputs)
             else:
                 if self._using_ports():
                     use_delayed = self.__check_dly_processing_prereq(inputs)
                     if use_delayed:
                         output_df = self.__delayed_call(inputs)
                     else:
-                        output_df = self.process(inputs)
+                        output_df = self.decorate_process()(inputs)
                 else:
                     # handle the dask dataframe automatically
                     # use the to_delayed interface
@@ -704,12 +718,12 @@ def __call__(self, inputs_data):
                     i_df = inputs[0]
                     rest = inputs[1:]
                     if isinstance(i_df, dask_cudf.DataFrame):
-                        d_fun = dask.delayed(self.process)
+                        d_fun = dask.delayed(self.decorate_process())
                         output_df = dask_cudf.from_delayed([
                             d_fun([item] + rest)
                             for item in i_df.to_delayed()])
                     else:
-                        output_df = self.process(inputs)
+                        output_df = self.decorate_process()(inputs)
 
         if self.uid != OUTPUT_ID and output_df is None:
             raise Exception("None output")
diff --git a/gquant/dataframe_flow/node.py b/gquant/dataframe_flow/node.py
index 50093b1e..8406a5f2 100644
--- a/gquant/dataframe_flow/node.py
+++ b/gquant/dataframe_flow/node.py
@@ -113,6 +113,7 @@ def __init__(self, task):
         self.delayed_process = False
         # customized the column setup
         self.columns_setup()
+        self.profile = False # by default, do not profile
 
         if self._using_ports():
             PortsSpecSchema.validate_ports(self.ports_setup())
diff --git a/gquant/dataframe_flow/task.py b/gquant/dataframe_flow/task.py
index 0aacda6c..db0d40f9 100644
--- a/gquant/dataframe_flow/task.py
+++ b/gquant/dataframe_flow/task.py
@@ -31,7 +31,7 @@ def __getitem__(self, key):
     def get(self, key, default=None):
         return self._task_spec.get(key, default)
 
-    def get_node_obj(self, replace=None, tgraph_mixin=False):
+    def get_node_obj(self, replace=None, profile=False, tgraph_mixin=False):
         """
         instantiate a node instance for this task given the replacement setup
 
@@ -39,6 +39,8 @@ def get_node_obj(self, replace=None, tgraph_mixin=False):
         -------
         replace: dict
             conf parameters replacement
+        profile: Boolean
+            profile the node computation
 
         Returns
         -----
@@ -97,7 +99,7 @@ def __repr__(self):
             node = NodeInTaskGraph(task)
         else:
             node = NodeClass(task)
-
+        node.profile = profile
         return node
 
 
diff --git a/gquant/dataframe_flow/taskGraph.py b/gquant/dataframe_flow/taskGraph.py
index 3dc9f313..5085d84d 100644
--- a/gquant/dataframe_flow/taskGraph.py
+++ b/gquant/dataframe_flow/taskGraph.py
@@ -225,7 +225,7 @@ def viz_graph(self, show_ports=False):
                         'shape': 'point'})
         return G
 
-    def build(self, replace=None):
+    def build(self, replace=None, profile=False):
         """
         compute the graph structure of the nodes. It will set the input and
         output nodes for each of the node
@@ -249,7 +249,8 @@ def build(self, replace=None):
         # instantiate node objects
         for task in self:
             task_id = task[TaskSpecSchema.task_id]
-            node = task.get_node_obj(replace.get(task_id), tgraph_mixin=True)
+            node = task.get_node_obj(replace.get(task_id), profile,
+                                     tgraph_mixin=True)
             self.__node_dict[task_id] = node
 
         # build the graph
@@ -304,7 +305,7 @@ def __str__(self):
             out_str += k + ": " + str(self.__node_dict[k]) + "\n"
         return out_str
 
-    def run(self, outputs, replace=None):
+    def run(self, outputs, replace=None, profile=False):
         """
         Flow the dataframes in the graph to do the data science computations.
 
@@ -314,6 +315,8 @@ def run(self, outputs, replace=None):
             a list of the leaf node IDs for which to return the final results
         replace: list
             a dict that defines the conf parameters replacement
+        profile: Boolean
+            whether profile the processing time of the nodes or not
 
         Returns
         -----
@@ -322,7 +325,7 @@ def run(self, outputs, replace=None):
         """
         replace = dict() if replace is None else replace
 
-        self.build(replace)
+        self.build(replace, profile)
 
         class OutputCollector(Node):
             def columns_setup(self):
diff --git a/gquant/plugin_nodes/strategy/xgboostStrategyNode.py b/gquant/plugin_nodes/strategy/xgboostStrategyNode.py
index b7e7610f..f6a9adbb 100644
--- a/gquant/plugin_nodes/strategy/xgboostStrategyNode.py
+++ b/gquant/plugin_nodes/strategy/xgboostStrategyNode.py
@@ -113,14 +113,13 @@ def process(self, inputs):
         train_cols = set(model_df.columns) - set(
             self.conf['no_feature'].keys())
         train_cols = list(train_cols - set([self.conf['target']]))
-        pd_model = model_df.to_pandas()
-        train = pd_model[train_cols]
-        target = pd_model[self.conf['target']]
-        dmatrix = xgb.DMatrix(train, target)
+        train = model_df[train_cols]
+        target = model_df[self.conf['target']]
+        dmatrix = xgb.DMatrix(train, label=target)
         bst = xgb.train(dxgb_params, dmatrix,
                         num_boost_round=dxgb_params['nround'])
         # make inferences
-        infer_dmatrix = xgb.DMatrix(input_df.to_pandas()[train_cols])
+        infer_dmatrix = xgb.DMatrix(input_df[train_cols])
         prediction = cudf.Series(bst.predict(infer_dmatrix)).astype('float64')
         signal = compute_signal(prediction)
         input_df['signal'] = signal
diff --git a/notebooks/01_tutorial.ipynb b/notebooks/01_tutorial.ipynb
index da3a3b4f..43f4fe13 100644
--- a/notebooks/01_tutorial.ipynb
+++ b/notebooks/01_tutorial.ipynb
@@ -65,11 +65,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys; sys.path.append('..')\n",
+    "import sys; sys.path.insert(0, '..')\n",
     "import warnings; warnings.simplefilter(\"ignore\")\n",
     "\n",
     "from gquant.dataframe_flow import TaskSpecSchema \n",
@@ -202,12 +202,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": 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PFu+//75ob2+/6dzPPfecACBOnz7d5Vw/1ZXj7+pnW1hYKOLi4oSDg4MIDAwU77zzjvF7XZ1LClVVVSIoKEjMnDlT0hxEFiRLJkQvuniEyMJUV1cjOjoanp6e2LdvX6+/8YTIHGpra5GQkACtVoucnBy4ublJHYnIEmTzmkIiCXl6eiI9PR2VlZUYO3YsLl68KHUkIkkVFhZizJgx0Gg0SE9PZyEkMiOWQiKJhYaGIjs7G05OThg6dCj+9re/oa2tTepYRGbV3t6OTZs2ITo6Gu7u7sjKykJwcLDUsYgsCkshUS/g5+eHzMxMrFu3DuvWrcP48eNRUFAgdaxe46e/f/hm24svvih1zD6Ts7c5d+4cYmNj8fTTT+Opp57CwYMHjY85IiLz4TWFRL3MmTNnsHz5cuTl5WH58uV4/vnnERQUJHUsom53+fJl/PWvf8VHH32E4cOH44MPPsCgQYOkjkVkqXhNIVFvExkZiezsbGzcuBGpqakYMGAAVqxYgeLiYqmjEXWLixcvYtmyZbj33nvx3Xff4Z133sGRI0dYCIkkxlJI1AvJ5XI8/vjjuHDhAt59913s378fAwcOxIIFC7Bnzx60t7dLHZGoS9ra2vDtt99i7ty5CAsLQ2ZmJjZt2oSioiI8+uijkMvlUkcksnhcPibqAwwGA7744gu89957yMzMRFBQEB555BEsW7aMS8vUq12+fBkffvghPvroI1y7dg3jx4/HE088gQULFrAIEvUu2SyFRH3M+fPn8cEHH2DLli2orq5GXFwc5syZg9mzZ/NuTeoVSkpKsH37dmzbtg1HjhyBt7c3li5dimXLlmHAgAFSxyOim2MpJOqrWltbkZqaii+//BKpqanQaDQYMWIE5syZgzlz5vD6LDKrM2fOYPv27di+fTu+//57uLq6Yvr06Zg/fz7uv/9+KBQKqSMS0c9jKSS6G7S1tSErKwtffvklvvrqK6jVavj4+CAuLg6JiYmYMmUKzyJSt6qoqMDhw4eRlpaGvXv3orS0FJ6enpg6dSoWLFiAKVOmwNraWuqYRHT7WAqJ7jbt7e3IycnBvn37sH//fmRlZUGv1yMsLAwTJ07EhAkTMHr0aAQGBkodlfqQK1euIDs7GwcPHkR6ejqKiopga2uLMWPGICEhAYmJiYiOjoaVFe9fJOqjWAqJ7nbNzc04cuQI0tPTsX//fpw8eRJtbW3w8/PDqFGjEBMTg5iYGIwcORJOTk5Sx6VeoKGhASdOnEB2djZycnKQk5OD8vJyyOVyREVFISEhARMnTkRsbCzs7OykjktE3YOlkMjSaLVa5ObmIjs7G8eOHcOxY8egVqshl8sRFhaGoUOHIjIyEkOGDMHgwYN5d/NdrrS0FHl5eTh79ixOnz6NM2fOoLCwEO3t7QgICMCoUaMwevRoxMTEICoqCg4ODlJHJqKewVJIREBZWRmOHTuG48eP48yZMzh79iyuXr0KAHBxccGQIUOMJXHAgAG45557EBQUxKXCPqKtrQ1XrlzBxYsXceHCBWMJzMvLg0ajAQAEBQUZ/51HjRqFUaNGwd/fX+LkRGRGLIVEdHPXr19HXl6ecTtz5gwKCgpQX18PALCxsUFoaKixJHb8GRwcjICAANja2kp8BJZFp9Ph6tWrKC0tNZa/ixcvoqioCMXFxbhx4wYAwNXVFRERERgyZAgiIyMxePBgDBkyBC4uLhIfARFJjKWQiLqmurraWDo6ikfH646zTgDg7e2NwMBABAQEICgoCEFBQQgICEBgYCC8vLzg4+MDR0dHCY+k79BqtSgvL0dVVRWuXr2KsrIyYwHs+Lqqqsq4v4uLi7GkdxT2jtceHh4SHgkR9WIshUTUfaqrq3HlyhWUlZWhtLQUV69eNZaY0tJSlJeXo62tzbi/nZ0dPD094ePjA09PT3h6esLb2xve3t5wdXWFi4sLVCqV8U9XV1eoVKo+u2zd3t6O+vp6k02j0UCj0aCurg6VlZWoqqpCdXU1qqqqUFFRgerqauh0OuMcCoUCvr6+CAoKQmBgoHHrOEMbFBTE4kdEd4KlkIjMx2AwoKKiAhUVFcbyU11dbSw/HV9XVVWhvr4eTU1NN53H2dnZWBatra3h4uICKysruLi4QC6Xw9nZGdbW1nBwcICtra3xDlmZTHbTZVI7O7tOy906nQ4tLS2d9q2vr0fHj83m5mbo9XpotVq0traioaEBbW1tqK+vR1tbGzQaDfR6PTQaDerr69HY2HjT43FwcICrqyu8vLzg7e1tLMi+vr7w9PSEh4eHsSz7+PjwQdBE1BNYComo92ptbTUWKo1Gg+vXr5u8/nHp6ihjBoMBjY2NuHHjBpqamtDS0mI809Yx9lONjY0wGAwmYwqF4qaP6HF0dIRSqQQAY+F0cHCAtbU1nJ2dIZfL4erqaiynNjY2Jmc7XVxcjFvHa5Y8IuoFWAqJiDrMnz8fCoUCW7dulToKEZG5ZffNC3OIiIiIqFuxFBIRERERSyERERERsRQSEREREVgKiYiIiAgshUREREQElkIiIiIiAkshEREREYGlkIiIiIjAUkhEREREYCkkIiIiIrAUEhERERFYComIiIgILIVEREREBJZCIiIiIgJLIRERERGBpZCIiIiIwFJIRERERGApJCIiIiKwFBIRERERWAqJiIiICCyFRERERASWQiIiIiICSyERERERgaWQiIiIiMBSSERERERgKSQiIiIisBQSEREREVgKiYiIiAgshUREREQElkIiIiIiAkshEREREQGQCSGE1CGIiMxt8+bN2LBhA9ra2oxjarUaMpkMvr6+xjG5XI61a9di8eLFUsQkIjKXbJZCIrJIhYWFCA8P/8X9ZDIZLly4gNDQUDOkIiKSTDaXj4nIIoWFhWHIkCGQyWS33Ecmk2H48OEshERkEVgKichiLVmyBHK5/Jbfl8vlWLJkiRkTERFJh8vHRGSx1Go1AgICcKsfgzKZDGVlZfDz8zNzMiIis+PyMRFZLj8/P4wZMwZWVp1/FFpZWSE+Pp6FkIgsBkshEVm05OTkm15XKJPJkJycLEEiIiJpcPmYiCxaXV0dvL29YTAYTMYVCgUqKyvh5uYmUTIiIrPi8jERWTY3NzckJiZCoVAYxxQKBe6//34WQiKyKCyFRGTxFi9ejPb2duPrtrY2PqyaiCwOl4+JyOI1NTXBw8MDOp0OAGBnZ4eamhrY29tLnIyIyGy4fExE5ODggJkzZ0KpVEKpVGLevHkshERkcVgKiYgALFq0CAaDAa2trXjwwQeljkNEZHaKX96FiKjvaWpqwo0bN3D9+nXo9Xo0NzcDAK5fv95p34aGBuj1etja2kImk6GhoQHbtm2Dk5NTp31dXV0BAPb29rCxsYGrqytsbGx4ZpGI+jxeU0hEvVZtbS0qKipQWVmJmpoa1NXV4fr16zf9U6vVGstdY2OjJHmdnJxgY2MDZ2dnODk5wdXVFW5ubjf908PDA97e3vD19eVdzkTUG2SzFBKR2QkhUF5ejpKSEpSUlKC0tBSVlZUoKytDZWUlrl27hoqKCuj1euPfkclkxlJ1s6Ll6OgIlUoFa2trODk5mZzJs7a2hoODA4AfituPHz8D/N9ZvwMHDkCpVGLcuHEmZxc7GAwGY+HUarW4ceMG6uvrjfs2Njbixo0b0Gg00Gq1qKurMymuP/76x2xtbeHj4wM/Pz/4+PjA398fPj4+CA4ORkhICEJCQuDr69sT/xRERB1YComoZ7S3t+Py5cvIz89HYWGhsQCWlJTg8uXLxjt9ra2tERAQAD8/P/j5+cHX19e4dbz29vaGh4eHWTIDuOmvvetu1dXVqKqqglqtRnl5ufHPjq/VajXKysrQ2toK4Ifi2FEQO7bw8HCEh4ejX79+N/2tLEREXcBSSES/XklJCU6fPo2CggKcO3cOBQUFKCwsNJ5p8/f3R//+/TuVmn79+sHf3x9yuVziI+id2tracO3aNZNC3bEVFxdDrVYD+OHu6bCwMERERCAiIgLh4eEYNmwYgoODJT4CIupDWAqJqGvUajVyc3ONW05ODqqqqgAAvr6+GDRoECIiIjBo0CD0798fQ4cOhaenp8Sp704ajQYXL17EuXPnkJ+fj+LiYpw7dw6FhYVob2+HSqXC4MGDERUVZdwiIiJ4VpGIboalkIhura2tDSdPnsShQ4eQkZGBo0ePora2FnK5HAMHDsTIkSONZWPo0KFwdHSUOjIBaGxsxOnTp3HixAljeT9//jza29vh6emJsWPHIj4+HuPHj8ewYcN4ppaIAJZCIvqx9vZ25ObmIj09HYcOHUJmZiYaGxvh6emJuLg4xMXFYeTIkRg2bBgLYB/T2NiI77//HidOnMDhw4dx+PBh1NbWwtnZGXFxcRg/fjwmTpyIqKgonkkkskwshUSWrrm5Genp6UhJSUFqaiquXbsGb29vREdHY9y4cUhMTMTw4cPNcvMFmVdxcTHS0tKQmZmJgwcP4urVq/D09MTUqVMxY8YMTJ48GSqVSuqYRGQeLIVElqiurg5ffPEFvvnmGxw6dAhtbW0YPXo0kpKSkJSUhMjISKkjkgROnTqF1NRUpKSkICcnB0qlEvHx8Zg/fz7mz59vfHA3Ed2VWAqJLIVer0dKSgr+/e9/49tvv4VSqcT06dMxY8YMTJ06Fe7u7lJHpF6kuroau3fvRkpKClJSUtDe3o6kpCQsXrwY999/P2xsbKSOSETdi6WQ6G534cIFbNy4EZ9++ikaGxuRkJCAxYsXY86cObwukG5LQ0MDvvnmG3zyySc4ePAgVCoVkpOTsWrVKoSGhkodj4i6B0sh0d1q//792LBhA1JTUxESEoInn3wSDz30EH8zBv0qZWVl+PTTT/Hee+/hypUrmDVrFlavXo3x48dLHY2Ifp1sXjlOdJfZs2cPRowYgYSEBGg0Gnz99dcoKirCs88+y0JIv1pAQACef/55XLx4EVu3bkVlZSXi4+MxatQopKenSx2PiH4FlkKiu8TZs2cxdepUTJs2DcHBwThx4gQyMjIwe/Zs3jlM3U4ul2PBggU4cuQIsrOz4eXlhcTEREyfPh0FBQVSxyOiO8D/UhD1cc3NzXjqqacwbNgw1NTU4ODBg9i2bRuioqKkjkYWIiYmBikpKUhLS8O1a9cQGRmJ1atXo6WlRepoRNQFLIVEfVheXh6io6OxdetWfPjhh8jJyUF8fLzUsSzC66+/DplMBplMhoCAAKnj9AoJCQnIzc3Fpk2b8PHHHyMmJoZnDYn6EJZCoj7qX//6F2JiYuDu7o5Tp05hyZIlfWaZWKvVYsCAAZg+fbrUUe7Y2rVrIYTA0KFDpY7Sq1hZWeGRRx7BqVOn4OjoiJEjR+Ljjz+WOhYR3Ya+8V8QIjLx8ssv47HHHsOzzz6L/fv3IzAwUOpIXSKEQHt7O9rb26WOQj0kODgYhw4dwtNPP41HHnkEr732mtSRiOgXKKQOQERd889//hPr1q3DP//5Tzz++ONSx7kjTk5OuHTpktQxqIcpFAr87W9/Q0BAAJ555hm4u7tj+fLlUscioltgKSTqQ06cOIFVq1bhL3/5S58thGR5fvvb36K6uhorV67EyJEjueRO1Etx+ZiojxBC4LHHHsP48ePxwgsvmP39t2/fbryxQiaTobS0FAsXLoSTkxPc3d2RnJyM69ev4/Lly5gxYwacnJzg6+uLFStWoLGx8Zbz6HS6m45fvnwZCxcuhIuLC9zd3TF9+vQun12sr683mVMmk+Hll18GABgMBpPx+fPnG/9ebW0t1qxZg9DQUFhbW8PV1RXTpk3DgQMHfvE9X375ZeOc48aNM47v2bPHOO7h4dHtn2uH6upqrFq1Cv369YO1tTU8PT0xd+5cnDp1qkufXXd78cUXERMTw/8zQ9SbCSLqE3bv3i1kMpk4ffq0pDlmzZolAIi5c+eKEydOCK1WK7Zs2SIAiGnTpolZs2aJ77//XjQ2Nor33ntPABC/+93vbjlPS0vLTcdnzenbB8AAACAASURBVJoljh49KrRardi3b5+ws7MT0dHRd5R56tSpwsrKSly8eLHT98aMGSM+++wz4+vy8nIREhIivL29xa5du4RGoxHnz58Xc+fOFTKZTLz//vsmf3/o0KHC39+/07wODg4iNja203hUVJRwd3fvNN4dn6tarRbBwcHC29tbpKamisbGRnH27FkRHx8vbG1txdGjR2/7M+sJJ06cEABEenq6pDmI6KayWAqJ+ogVK1aI0aNHSx3DWF5SU1NNxgcNGiQAiIyMDJPxkJAQMXDgwFvOc6tSuGvXLpPx+fPnCwCiurq6y5nT0tIEALFy5UqT8czMTBEUFCRaW1uNYw8//LAAID7//HOTfXU6nfDz8xN2dnaioqLCON7dpfDXfK5Lly4VAMSnn35qMl5eXi5sbGxEVFRUp/c1txEjRoinnnpK6hhE1FkWl4+J+ogzZ85g7NixUscwGjlypMlrPz+/m477+/tDrVZ3ef7o6GiT1x13WN/JXAkJCRg+fDg2b96M2tpa4/j69euxevVqKBT/d3n1tm3bAABJSUkmc9jY2CAhIQEtLS3Yu3dvlzPcrl/zuW7fvh1WVladHvXj4+ODQYMGITc3F2VlZT2Q+vaNHTsWp0+fljQDEd0cSyFRH9HY2AgnJyepYxg5OzubvLaysoJcLoe9vb3JuFwuv6NHz6hUKpPX1tbWAHDHj7F59tln0dzcjHfffRcAUFRUhEOHDuHRRx817qPX66HRaGBra3vTz9rb2xsAUFFRcUcZbsedfq4d2dvb26FSqTpdS3ny5EkAwIULF3os++1QqVQ3vRaSiKTHUkjUR3h7e0t+lqcvW7hwIQIDA/H2229Dr9fjjTfewIoVK0zKn42NDVQqFXQ63U2LS2VlJYAfzrz9EisrK9y4caPTeH19/a84iluzsbGBi4sLFAoFWltbIYS46Xbffff1yPvfritXrtzW50dE5sdSSNRHxMXF4bvvvkNbW5vUUfokhUKBZ555BlVVVXjjjTewdetWrFq1qtN+c+bMAQCkpqaajOv1eqSnp8POzg5Tpkz5xffz9fXFtWvXTMYqKipw5cqVX3EUP2/u3LkwGAw4cuRIp+/97W9/Q1BQEAwGQ4+9/y9pbW1FWloa4uLiJMtARLfGUkjURyQnJ0OtVuOLL76QOkqf9dhjj0GlUuFPf/oTZs+eDX9//077vPrqqwgJCcHq1auRkpKCxsZGFBUV4aGHHkJ5eTk2btxoXEb+OZMnT4Zarcbbb78NrVaLS5cu4ZlnnoGXl1dPHJoxe2hoKJYtW4bdu3dDo9Ggrq4O//u//4u//OUveP31102unzS3Tz/9FDU1NVi0aJFkGYjoZ0h5mwsRdc3y5cuFr6+vqKmpMft7Z2VlCQAm2wsvvCCOHz/eafzVV18Vhw8f7jT+5z//WWzbtq3T+KJFi245vxCi03hSUtIdH8dzzz0nAPzso31qamrE6tWrRUhIiFAqlUKlUokpU6aYPEpl/fr1t8wrhBD19fXi0UcfFb6+vsLOzk6MGzdOHD9+XERFRRn3f/7557vtc+1QW1sr1qxZI/r37y+USqXw9PQUkydPFvv27bvjz6w7VFZWCk9Pz053gBNRr5ElE0KIHuqbRNTN6uvrMWzYMPTr1w979+6FjY2N1JGIflFLSwsSExNRWVmJ77//vlfdMEVERtlcPibqQ1xcXJCSkoLTp09j5syZaG5uljoS0c/SarVISkpCYWEhUlJSWAiJejGWQqI+ZvDgwUhPT8fJkycxevRo5OfnSx2J6Kby8vIwatQo5Ofn48CBAwgLC5M6EhH9DJZCoj5oxIgROHnyJJydnTFy5Ehs3LhR6khm99Pn8N1se/HFF6WOabG2bNmC0aNHw9nZGVlZWYiMjJQ6EhH9Al5TSNSHGQwG/OlPf8L69esxdepUrF+/HhEREVLHIgt29uxZrF27Fvv27cMLL7yAdevWSXrHMxHdNl5TSNSXKRQK/PWvf0V6ejrUajWGDh2KJ598ElVVVVJHIwtTXl6OFStWYNiwYaitrUVGRgb+8pe/sBAS9SEshUR3gQkTJiA3Nxfvv/8+du3ahXvuuQfPPfdcjz4omQgAiouLsXr1atx777347rvvsHnzZuTk5GDcuHFSRyOiLuLyMdFdprm5GW+//Tb+8Y9/oKKiAvPmzcPvfvc7xMTESB2N7iKZmZnYsGEDtm/fjoCAAKxatQpPPvkk7OzspI5GRHcmm6WQ6C7V2tqKL7/8Em+99RZOnDiBkSNHIjk5GQ888ECP/lYNunuVl5dj69at2LJlC06dOoWYmBisWbMGc+fO5TIxUd/HUkhkCTIzM/HBBx/gm2++QXNzMyZPnoxFixZh9uzZsLe3lzoe9WJarRbbtm3Dv//9b6Snp8PR0RHz5s3Do48+ijFjxkgdj4i6D0shkSXR6XTYt28fPvnkE+zYsQNWVlYYN24cpk+fjjlz5iAoKEjqiNQLlJaWYu/evUhLS8Pu3bvR0tKC++67D8nJyZg3bx4cHBykjkhE3Y+lkMhS1dTUYMeOHUhNTcW+ffug1WoxdOhQJCUlYcqUKRg1ahRsbW2ljklm0NLSgmPHjmHv3r1ITU1FXl4enJycMHnyZCQlJWHmzJlwd3eXOiYR9SyWQiIC9Ho9MjIykJKSgpSUFJSUlMDW1hbR0dGYMGEC4uLiMHbsWJ4huktotVocPXoUhw4dQkZGBo4fPw69Xo/Q0FBMnz4dSUlJiI+Ph7W1tdRRich8WAqJqLPLly8jIyMDGRkZOHToEC5dugSlUokRI0YgKioKI0eORFRUFCIiIniDQS9nMBhw7tw5nDhxArm5uTh+/DhOnToFg8GAAQMGYPz48YiPj8f48eMRHBwsdVwikg5LIRH9smvXriEjIwNZWVk4ceIETp8+jZaWFtjZ2WHYsGGIiorCsGHDMGjQIISHh0OlUkkd2SLV19ejoKAA586dw6lTp5Cbm4tTp05Bp9PB3t7e+G81duxYxMfHw9fXV+rIRNR7sBQSUdcZDAbk5+cbzz7l5uYiLy8Pzc3NAAB/f3+Eh4cjIiICERERCAsLQ2hoKPz9/SGTySRO37e1t7fj2rVruHTpEgoLC5Gfn4/8/HwUFBRArVYDABwcHDBkyBDjGd2oqCiEh4fzrC4R/RyWQiLqHu3t7bh8+TIKCgqMJeXcuXMoLCxEQ0MDAMDGxgb9+vVDSEiIyRYcHAx/f394eXlZfHExGAyorKzEtWvXUFpaipKSEpPt8uXLuHHjBgBApVIhPDwcgwYNQlhYmPFMbXBwMMs3EXUVSyER9Ty1Wo3i4mIUFxebFJzi4mKo1Wq0t7cDAKysrODl5QUfHx/4+/vD29vbWBY9PDzg6uoKNzc34+bq6irxkd2euro61NXV4fr168ava2trUVVVhbKyMmMJrKysRGVlJTp+LFtZWcHf3x/9+/c3KdH9+/dH//79ufxLRN2JpZCIpKXX61FWVoby8nKo1WpUVFSYfK1Wq1FVVYXa2lq0tbWZ/F2ZTGYshyqVCg4ODrC2toarqyusra3h4OAAR0dHWFtbw8XFBcAPReun1zwqFAo4OTmZjDU2NsJgMJiM1dfXGwtbfX09bty4Aa1Wi6amJuj1etTX10Ov16O5uRkajcZYAH9KLpfD3d0dXl5e8Pf3h4+PD/z8/ODj4wNfX1/jFhgYyDuAichcWAqJqO/QaDQmZ9t+fAZOo9GgubkZer0e169fx40bN9DU1ITGxkbo9XrjEnbH+I/pdDq0tLSYjNnb28PGxsZkzNHREUqlEsAPS7fW1tZwcnKCg4MDbGxs4OLiAhsbG9jb20OlUnU6q9nxtbOzcw9+SkREd4SlkIiow/z586FQKLB161apoxARmVu2ldQJiIiIiEh6LIVERERExFJIRERERCyFRERERASWQiIiIiICSyERERERgaWQiIiIiMBSSERERERgKSQiIiIisBQSEREREVgKiYiIiAgshUREREQElkIiIiIiAkshEREREYGlkIiIiIjAUkhEREREYCkkIiIiIrAUEhERERFYComIiIgILIVEREREBJZCIiIiIgJLIRERERGBpZCIiIiIwFJIRERERGApJCIiIiKwFBIRERERWAqJiIiICCyFRERERASWQiIiIiICSyERERERgaWQiIiIiMBSSEREREQAZEIIIXUIIiJz27x5MzZs2IC2tjbjmFqthkwmg6+vr3FMLpdj7dq1WLx4sRQxiYjMJZulkIgsUmFhIcLDw39xP5lMhgsXLiA0NNQMqYiIJJPN5WMiskhhYWEYMmQIZDLZLfeRyWQYPnw4CyERWQSWQiKyWEuWLIFcLr/l9+VyOZYsWWLGRERE0uHyMRFZLLVajYCAANzqx6BMJkNZWRn8/PzMnIyIyOy4fExElsvPzw9jxoyBlVXnH4VWVlaIj49nISQii8FSSEQWLTk5+abXFcpkMiQnJ0uQiIhIGlw+JiKLVldXB29vbxgMBpNxhUKByspKuLm5SZSMiMisuHxMRJbNzc0NiYmJUCgUxjGFQoH777+fhZCILApLIRFZvMWLF6O9vd34uq2tjQ+rJiKLw+VjIrJ4TU1N8PDwgE6nAwDY2dmhpqYG9vb2EicjIjIbLh8TETk4OGDmzJlQKpVQKpWYN28eCyERWRyWQiIiAIsWLYLBYEBraysefPBBqeMQEZmd4pd3ISK6+1RVVaGqqgoajQYtLS2wsrKCra0tZDIZhBBIS0uDvb09nJ2d4eXlBS8vL6kjExH1KF5TSER3rWvXriE3NxeFhYU4f/48CgoKUFpaiqqqqk6PoPklSqUSXl5e6NevH8LCwjBw4ECEh4cjKioKvr6+PXQERERmk81SSER3jeLiYuzevRtHjhzBkSNHcOXKFQBAUFAQBg4ciIEDByI0NBReXl7w9/eHl5cXXFxcYGtrC7lcjtzcXCiVSkRGRqKtrQ0tLS2or69HVVUV1Go1qqqqcPHiRZw/fx6FhYUoKysDAPTr1w+xsbGIjY3FtGnT0K9fPwk/BSKiO8JSSER9W3Z2Nr766iukpqaisLAQKpUKsbGxGD16NMaOHYvo6Gg4Ozvf1lwdj6W52a+9uxmNRoOcnBxkZWUhOzsbmZmZaGxsREREBJKSkrBgwQJER0ff8bEREZkRSyER9T1qtRoff/wxtmzZgsLCQoSFhWHGjBmYNm0axo0bB6VSKUmu1tZWHD58GKmpqdi1axcuXLiAiIgILF26FEuWLIGPj48kuYiIbgNLIRH1HXl5eXj77bexZcsW2Nra4je/+Q2Sk5Mxbtw4qaPdVG5uLrZs2YLPP/8cGo0GCxcuxPPPP49BgwZJHY2I6KdYComo9ysoKMDvf/977Nq1C+Hh4Xj22WexaNEi2NjYSB3ttuh0OnzyySd44403UFRUhLlz5+LVV1/FgAEDpI5GRNSBD68mot6rtrYWTz31FCIjI3H58mXs3LkTZ8+exbJly/pMIQQAW1tbrFixAvn5+di2bRsKCwsxaNAgrFq1CnV1dVLHIyICwEfSEFEv9c0332DlypWwsrLCSy+9hIcffhhyuVzqWN2ira0NH3zwAf785z9DJpPhvffew8yZM6WORUSWjWcKiah30Wq1WLRoEebNm4ekpCQUFBRg+fLld00hBAC5XI7HHnsMBQUFmDRpEmbNmoWHH34Yzc3NUkcjIgvGM4VE1GuUlJRg1qxZqKysxObNmzFt2jSpI5nFrl27sGzZMgQGBmL79u0ICgqSOhIRWR6eKSSi3uHYsWMYNWoUFAoFjh8/bjGFEABmzJiBnJwcGAwGREdH4+TJk1JHIiILxFJIRJI7duwYpkyZgpiYGGRmZlrkmbKQkBAcPXoUw4YNw6RJk1gMicjsuHxMRJI6c+YMxo8fj7i4OHz11Vd96q7inqDT6TBr1iycOHECmZmZCA8PlzoSEVkGPqeQiKRz/fp1REdHIyAgAHv37rX4QtihpaUFiYmJqKurw7Fjx2771/QREf0KvKaQiKSzdOlS6PV6/Oc//2Eh/BE7Ozt8+eWXqK+vx7Jly6SOQ0QWgqWQiCTxn//8B6mpqdi6dSu8vLykjtPr+Pn54bPPPsM333yDHTt2SB2HiCwAl4+JyOy0Wi3CwsIwdepU/Otf/5I6Tq+2ePFiHDlyBPn5+bCzs5M6DhHdvbh8TETm9+GHH0Kj0eDVV1+VOkq3cHR0xLhx43pk7vXr16OiogKffPJJj8xPRNSBpZCIzEoIgXfffRfJycnw9PSUOk6v5+vriwcffBB///vfwYUdIupJLIVEZFZZWVk4f/48nnjiCamj9BlPPvkkzp07x2cXElGPYikkIrPav38/AgMDERkZ2a3z6vV6rFu3DmFhYbC3t4ebmxtmzJiBnTt3oq2tzWTf2tparFmzBqGhobC2toarqyumTZuGAwcOGPfZvn07ZDKZcTt//jx+85vfwN3d3Tj2+9//HjKZDE1NTThy5IhxXKFQdOuxjRw5Et7e3ib5iIi6G0shEZnV4cOHMWHChG6f9+mnn8bf//53/OMf/0BtbS0KCgoQFhaGWbNm4fDhw8b9KioqEB0djc8++wwbN25ETU0Njh07Bnt7eyQkJBhvfJk9ezaEEJg1axYA4PHHH8fKlStx9epVZGdnQy6XY+3atRBCwMHBAbGxsRBCQAgBg8HQrccmk8kQHx+PjIyMbp2XiOjHWAqJyKwuXbqEiIiIbp83PT0dgwYNwqRJk2BnZwdvb2+sX78e9957r8l+f/jDH1BSUoINGzZg+vTpcHZ2xr333ovPPvsMvr6+WLVqFSorKzvN//zzz2PChAmwt7dHTEwMDAYDPDw8uv04biU8PBwlJSVmez8isjwshURkVtXV1T1SpqZOnYqjR4/iscceQ3Z2tnHJ+Pz58yZnJrdt2wYASEpKMvn7NjY2SEhIQEtLC/bu3dtp/lGjRnV75q7w8vJCVVWVpBmI6O7GUkhEZtXc3AwHB4dun/edd97Bli1bUFxcjISEBDg7O2Pq1KnGEgj8cN2hRqOBra0tnJycOs3h7e0N4Icl5p/qicxd4eDgAK1WK2kGIrq7sRQSkVm5ubmhtra22+eVyWRITk5GWloa6uvrsX37dgghMHfuXLz55psAfjgbqFKpoNPp0NjY2GmOjmVjHx+fLr93T6upqeEjfIioR7EUEpFZ9dQyqIuLCwoLCwEASqUSkyZNMt5BnJqaatxvzpw5AGAyBvxwFjE9PR12dnaYMmVKl97b3t4eN27cML4eOHAgNm3adKeHclNVVVUshUTUo1gKicisIiMjkZOT0yNzP/HEEzhz5gz0ej2qqqrw2muvQQiBiRMnGvd59dVXERISgtWrVyMlJQWNjY0oKirCQw89hPLycmzcuNG4jHy7RowYgaKiIly9ehVZWVkoLi5GXFxctx5bTk4Ohg4d2q1zEhH9GEshEZnVhAkTcOTIEbS2tnbrvBkZGQgLC8MDDzwANzc3hIeHY8+ePXj//ffxxz/+0bifj48Pjh8/jgcffBCrVq2Cu7s7Ro0ahaamJqSlpWHFihUAgOzsbMhkMuzYsQMAYGdnd8tl4g0bNiAyMhLh4eFYuHAhNm7ciPDw8G47Np1Oh+zsbMTHx3fbnEREPyUT/L1JRGRGpaWl6N+/P7766ivjUi79vK1btyI5ORmlpaXw8/OTOg4R3Z2yWQqJyOymT58OnU6HtLQ0qaP0CbGxsfDz88OXX34pdRQiuntlc/mYiMzut7/9Lfbv34+jR49KHaXXO3jwII4ePYqnn35a6ihEdJfjmUIiksS0adNQWVmJ48ePQy6XSx2nVzIYDIiKikJwcDB27twpdRwiurvxTCERSeOtt97CuXPn8Prrr0sdpdd65ZVXUFRUhLfeekvqKERkAVgKiUgSYWFhePXVV/HCCy8gPT1d6ji9zrfffov//u//xuuvv47Q0FCp4xCRBeDyMRFJ6sEHH0RaWhoyMjIQEREhdZxe4fTp07jvvvswc+ZMbN68Weo4RGQZePcxEUmrubkZU6dORVFREfbv32/xxfDMmTNISEjA0KFDkZKSAltbW6kjEZFl4DWFRCQte3t7fPvtt7j33nsxceJEZGVlSR1JMocPH0ZCQgIiIyOxc+dOFkIiMiuWQiKSnKOjI7799ltER0fjvvvuw0cffSR1JLPbtGkTEhMTERcXh127dsHe3l7qSERkYVgKiahXcHR0xI4dO7BmzRosX74cS5cuRX19vdSxelxdXR0WLVqEJ554An/4wx/w9ddfsxASkSR4TSER9Tq7du3CY489Brlcjn/+85+YMWOG1JF6xPbt27Fy5UpYWVnh/fffx7Rp06SORESWi9cUElHvM2PGDJw7dw7x8fGYOXMmJk+ejDNnzkgdq9ucPHkSCQkJmDNnDhITE5GXl8dCSESSYykkol7Jzc0Nn376KTIyMlBfX4/hw4dj8eLFOH36tNTR7lhubi4WLlyI6OhoNDc3IzMzE1u2bIGrq6vU0YiIWAqJqHcbP348jh07hk8//RR5eXkYNmwYJk+ejJSUFBgMBqnj/aLW1lbs3LkTCQkJGDlyJIqKirB161YcPXoUsbGxUscjIjJiKSSiXk8mk+GBBx7AqVOnsGfPHgghMHPmTAQGBmLNmjU4efKk1BFNCCFw4sQJrF69Gv7+/pgzZw6USiX27duH77//HgsWLIBMJpM6JhGRCd5oQkR9UklJCbZs2YJPPvkEly5dQkBAAJKSkpCUlITx48dDpVKZNU99fT0yMjKQmpqK1NRUqNVqDBgwAEuWLEFycjKCg4PNmoeIqIv4G02IqG8TQuDkyZNITU1FSkoKcnNzAQDh4eEYPXo0YmJiEB4ejvDwcLi7u3fLe9bU1KCgoAD5+fnIyclBdnY2CgoKIJPJEB0djenTpyMpKQnDhw/vlvcjIjIDlkIiurtUVVUhKysLR48eRVZWFr7//ntotVoAgIeHB+655x54eXnB19cXPj4+cHJygpOTEwAYb/i4fv06AKChoQGNjY2oqKhARUUFKisrcfHiRdTW1gIAnJycMGLECIwdOxajR4/G2LFj4eHhIcFRExH9aiyFRHT3u3LlCs6fP4/CwkKUlJSgqqoK5eXlqKyshFarRUNDA4QQxodlu7i4QCaTQaVSwcHBAT4+PvD19YWXlxdCQkIQFhaGgQMHIjAwUOIjIyLqNiyFREQd5s+fD4VCga1bt0odhYjI3PjwaiIiIiLiI2mIiIiICCyFRERERASWQiIiIiICSyERERERgaWQiIiIiMBSSERERERgKSQiIiIisBQSEREREVgKiYiIiAgshUREREQElkIiIiIiAkshEREREYGlkIiIiIjAUkhEREREYCkkIiIiIrAUEhERERFYComIiIgILIVEREREBJZCIiIiIgJLIRERERGBpZCIiIiIwFJIRERERGApJCIiIiKwFBIRERERWAqJiIiICCyFRERERASWQiIiIiICSyERERERgaWQiIiIiMBSSERERERgKSQiIiIiADIhhJA6BBGRuW3evBkbNmxAW1ubcUytVkMmk8HX19c4JpfLsXbtWixevFiKmERE5pLNUkhEFqmwsBDh4eG/uJ9MJsOFCxcQGhpqhlRERJLJ5vIxEVmksLAwDBkyBDKZ7Jb7yGQyDB8+nIWQiCwCSyERWawlS5ZALpff8vtyuRxLliwxYyIiIulw+ZiILJZarUZAQABu9WNQJpOhrKwMfn5+Zk5GRGR2XD4mIsvl5+eHMWPGwMqq849CKysrxMfHsxASkcVgKSQii5acnHzT6wplMhmSk5MlSEREJA0uHxORRaurq4O3tzcMBoPJuEKhQGVlJdzc3CRKRkRkVlw+JiLL5ubmhsTERCgUCuOYQqHA/fffz0JIRBaFpZCILN7ixYvR3t5ufN3W1saHVRORxeHyMRFZvKamJnh4eECn0wEA7OzsUFNTA3t7e4mTERGZDZePiYgcHBwwc+ZMKJVKKJVKzJs3j4WQiCwOSyEREYBFixbBYDCgtbUVDz74oNRxiIjMTvHLuxAR9U1arRY6nQ4NDQ3QarVobW2FTqdDS0uLyX5NTU1obm6Gra0tZDIZGhoasHPnzk5nC+3t7WFjYwNra2s4ODjA2dkZdnZ2cHBwMOdhERH1CF5TSES9Xk1NDdRqNdRqNWpra1FbW4u6ujqTr2tqanD9+nWTImhOPy6Ibm5ucHd3N24/fu3m5gZ/f3/4+fnB3d3drBmJiH5GNkshEUlKr9ejpKQEly5dwsWLF1FWVga1Wo2rV69CrVbj2rVrxhtAAECpVN60bHW8dnR0hK2tLZydneHg4ABbW1uoVCrjWT6FQgEnJyeTDDY2NrC3t8eBAwegVCoxbtw4NDc3Q6/Xm+zX2NgIg8EAvV6P5uZmaDQa6HQ6NDU1oaGhATqdDlqt9pbFtba2Fq2trcb5bG1tERAQAF9fXwQFBcHPzw8BAQEIDQ3FPffcg379+sHGxqZn/wHo/7F333FRXen/wD9DHXqT3sSGhaaoFEFUECRiwxg0MYlJkKwmiiam7W52k2/auikGjRtjSTHJRmMUI2JBilGaAtJVwKBSR4ow9Drn94e/ucsIqAjDpTzv12tezNw5c+9zhzn3PLeccwkh91BSSAgZHLdu3UJ2djZyc3Px559/co+SkhJuOBhjY2NYWVlxSZKpqSksLCy4I2vm5ubQ1taWW4zSOHq67d1AEYvFXLIrTX7Ly8u5JLioqAgVFRVcHJaWlhg/fjz3sLOzg52dHaytreUWIyFkVKKkkBAysBobG3HlyhVkZ2cjKysL2dnZyMnJ4U7nWltbc0fCpImO9LmmpibP0Q8N9fX1uHHjBpc4S5/fuHEDxcXFAAAdHR3Y2dnB3t4eDg4OsLe3x4wZM6jXNCHkcVFSSAjpn8LCQsTHxyMtLQ1paWlISUlBW1sbdHR0MGHCBEydOhXOzs6YW8aA6QAAIABJREFUNm0aHB0dYWhoyHfIw1pdXR0KCgqQm5uLtLQ0XL16FZmZmaisrISioiJsbW3h7OwMZ2dneHh4YPr06XI98kkIGTEoKSSEPDrGGLKyshATE4Po6GgkJCSgrq4O6urqmDFjBlxcXLiHlZUV3+GOKjdv3sSlS5dw6dIlXL58GVeuXEFLSwt0dHTg6ekJb29veHt7w87ODgKBgO9wCSFDDyWFhJAHKy0txenTpxEdHY3Y2FhUVlZizJgxmD9/PubNmwdXV1c4ODjI3DuY8K+9vR2ZmZlITk7G+fPnERcXh7t378LY2BgLFiyAj48P/P39YWpqyneohJChgZJCQkh3N2/exIkTJ3DkyBEkJSVBVVUVc+bMgY+PD3x8fOiU5DAkkUiQnp6O+Ph4JCQk4OzZs2hoaMD06dMREBCA1atXY/LkyXyHSQjhDyWFhJB7bt26he+//x6//fYbcnNzYWhoiCVLlmDFihXw8fGBUCjkO0QygJqbm3Hu3DmEh4cjIiIC1dXVsLe3x6pVq7Bu3TpYWlryHSIhZHBRUkjIaNba2orjx4/jwIEDiImJgZGREYKCgrBixQp4eHhAUVGR7xDJIOjo6MCFCxcQHh6Ow4cPo7q6Gr6+vnjppZewdOlSqKio8B0iIUT+KCkkZDQSiUTYsWMHDhw4gNraWvj7+yM4OBhPPPEElJWV+Q6P8KitrQ0RERE4cOAAoqKioK+vj/Xr1yM0NBRGRkZ8h0cIkR9KCgkZTW7evInPPvsM3377LXR1dbFp0yasW7cOZmZmfIdGhqCSkhJ8++232L17NxoaGhAcHIzXX3+depYTMjJRUkjIaFBZWYl33nkHP/zwAywsLPDGG2/gxRdfpOsEySNpamrC/v378fnnn6O8vBwvvfQSPvzwQ7p3MyEjCyWFhIxknZ2d+Oabb/D3v/8dGhoa+Oijj/D000/T8DHksbS1teHHH3/Eu+++i7a2NnzyySd46aWXqCc6ISMDJYWEjFTXr1/HM888g+zsbGzduhXvvvsu3UaODIi6ujq899572LVrF2bMmIGffvoJEydO5DssQkj/JNPuHSEj0LFjxzB79myoqKggMzMT27dvH7IJ4aFDhyAQCCAQCB7pdHZfy5OBp62tjS+++ALp6emQSCSYPXs2Tp48yXdYhJB+oqSQkBGEMYa//vWvePLJJ7F27Vr88ccfmDJlCt9hPdDq1avBGIO3t7dcyhP5sbOzw8WLF7Fy5UosXboU77//Pt8hEUL6gS4sImQE2bx5M7755ht8++23WLduHd/hjCqamppwcnJCfHw836EMKqFQiP3792P27Nl45ZVX0NjYiH//+998h0UIeQyUFBIyQuzYsQNff/01fv31VwQGBvIdDhllQkJCoKmpiWeffRY2NjbYsGED3yERQvqIkkJCRoDMzEy8+eab+OijjyghJLx5+umnUVBQgC1btsDLywtTp07lOyRCSB/QNYWEjAChoaGYPXs23njjjUFfdkdHBw4fPoyFCxfCxMQEampqsLe3R1hYGCQSSbfy169fx/Lly6GjowMNDQ14eno+8JRrX8s/zPHjx7mOKgKBAHl5eXjqqadgYGDATauqqgJwb3zHzZs3Y+zYsVBRUYGhoSECAwORkZHBze+zzz6DQCBAY2MjEhISuHlIh/358MMPuWkeHh7c586cOcNNHzNmTJ/i279/v0yZW7duISgoCLq6ujAwMEBAQAD+/PPPx/6O+uPvf/87HBwc8Nprr/GyfEJIPzBCyLCWnp7OALC4uDhelh8REcEAsI8//pjdvXuXVVZWsp07dzIFBQW2bds2mbIFBQVMV1eXmZubs6ioKFZfX8+ysrKYr68vGzt2LFNVVe1X+b5YtmwZA8C8vLxYXFwca2xsZMnJyUxRUZFVVlaysrIyZm1tzYyNjVlkZCSrr69nOTk5zMvLiwmFQpaYmCgzPw0NDTZnzpxel9fb+87OzszAwKDP8XUts2zZMpaYmMgaGhrYuXPnmJqaGps1a9Zjfzf9debMGSYQCFhubi5vMRBC+iyJkkJChrkPPviAWVlZ8bb8iIgINm/evG7T165dy5SVlZlYLOamrVq1igFgv/32m0zZ0tJSpqqq2i3J62v5vpAmVKdOnerx/eeff54BYD///LPM9PLycqaqqsqcnZ1lpssrKewtvq5lIiIiZKY/+eSTDACXPA42iUTCjI2N2fbt23lZPiHksSTR6WNChrmcnBzMnDmTt+UHBAQgLi6u23RHR0e0t7cjNzeXm3bmzBkAgJ+fn0xZMzMzTJo0qds8+lr+ccyePbvH6cePH4eCggICAgJkppuYmGDatGlIS0tDSUnJgMTwOPF1NWvWLJnXlpaWAICysjK5xPQwAoEAs2bNQk5ODi/LJ4Q8HupoQsgw19DQIHNN2mATi8X4/PPPER4ejpKSEtTW1sq839TUBABobW1FfX09hEJhjwNpGxkZIT8/n3vd1/KPS0NDo9u01tZWiMViAICOjk6vny0oKICFhUW/Y3iQnuK73/0xqqioAECP13QOFi0tLdTX1/O2fEJI39GRQkKGOSMjI96OCAHAkiVL8MEHH2D9+vXIz8+HRCIBYww7duwAcG9AbQBQVVWFlpYWWlpa0NDQ0G0+d+/elXnd1/IDSVVVFbq6ulBSUkJ7ezsYYz0+5s+fz31GIBA8cJ4KCgpoa2vrNv3+JHqkKC0thYmJCd9hEEL6gJJCQoY5Nzc3JCUlcUfkBlNnZycSEhJgYmKCzZs3w9DQkEuOmpubu5X39/cH8L/TwlJVVVXIy8vrd/mBFBgYiI6ODiQkJHR7b/v27bCyskJHRwc3TV1dXSbps7W1xd69e7nXpqamKC0tlZmPSCRCUVGRHKLnV319PS5fvgw3Nze+QyGE9AElhYQMc8uXL0dnZyd++OGHQV+2oqIi5s2bB5FIhE8//RRVVVVobm5GXFwc9uzZ0638xx9/DH19fWzZsgXnzp1DQ0MDrl69irVr1/Z4iriv5QfSJ598gvHjx+PFF1/E6dOnIRaLcffuXXzzzTf4v//7P3z22WfcsDMAMGPGDOTn56O4uBhJSUkoLCyEp6cn976vry/Kysrw1VdfoaGhAX/++SdCQ0NhZGQk1/Xgw/79+6GkpISlS5fyHQohpC/46+RCCBkooaGhzNDQkJfeppWVlezll19mlpaWTFlZmRkbG7N169axt99+mwFgAGR66ubl5bHly5czbW1tbuiUkydPMm9vb678Sy+99NjlHyYpKYn7XNdHT6qrq9lrr73Gxo0bx5SVlZmhoSHz9fVl586d61b2+vXrzNPTk2loaDBLS0u2e/dumfdra2tZcHAwMzU1ZWpqaszDw4OlpKQwZ2dnLoa33nrrkeLrqczf/vY3xhjrNn3x4sWP/N0MhPLycqanp8fefPPNQV0uIaTfkgSM/f8Lfgghw1ZdXR0cHBxga2uLU6dOQVFRke+QyCjU3t6OhQsXoqSkBJmZmY/USYYQMmQk0+ljQkYAbW1tHD16FPHx8Vi3bh06Ozv5DomMMh0dHXj66adx5coVhIeHU0JIyDBESSEhI4SzszN+//13HD16FEuWLEFNTQ3fIZFRoqqqCosWLcLp06cRGRkJe3t7vkMihDwGSgoJGUF8fHyQkJCAa9euwcnJCampqXyHNGi63gu4t8d7773Hd5gjTkZGBmbPno38/HzExcXJdK4hhAwvlBQSMsJMnz4dly9fxvjx4+Hl5YWPP/4Yra2tfIcld6yXsQS7PigpHDgtLS14//334ebmhgkTJiA9Pb3bnVUIIcMLJYWEjECGhoaIiorCu+++i48//hj29vbdxvoj5HFFRERg2rRp+Pzzz/HBBx/g9OnTMDAw4DssQkg/UVJIyAilpKSEt99+G9euXYOjoyP8/f3h7++P+Ph4vkMjw9T58+fh4+ODpUuXwsXFBdevX8e2bduotzshIwQlhYSMcJaWljhy5Aiio6PR0NAAT09PzJ07F6dPn+Y7NDIMMMZw8uRJuLu7Y/78+ejs7MT58+fx3//+F2ZmZnyHRwgZQJQUEjJKeHt74+LFi7hw4QI0NDTwxBNPwMHBATt37kR1dTXf4ZEhprKyEjt27ICdnR2WLl0KAwMDJCYmIi4uDl5eXnyHRwiRAxq8mpBRKj09Hbt378avv/6KtrY2LF++HC+++CJ8fHygoED7i6NRZ2cnoqKi8O233+LEiRMQCoVYs2YNNm7cCAcHB77DI4TIVzIlhYSMci0tLYiIiMDevXsRExMDAwMD+Pv7Y9WqVfDz84OKigrfIRI56uzsRFJSEo4cOYIjR46gvLwczs7OCAkJwdNPPy33e0wTQoYMSgoJIf9z/fp1/PbbbwgPD8eVK1egq6uLgIAALFu2DAsWLIC+vj7fIZIBUFVVhdjYWBw/fhynTp1CXV0dnJ2dERgYiCeffBITJ07kO0RCyOCjpJAQ0rNbt27h+PHjCA8PR0JCAhhjmD59Ory9veHt7Q0PDw+oq6vzHSZ5BI2Njbh48SJiYmIQExODzMxMCAQCeHp6YsWKFVi+fDmsrKz4DpMQwi9KCgkhD1dbW4vz589zScW1a9egqqqK2bNnw8XFBa6urnBxcYGFhQXfoRIARUVFSE5OxqVLl3Dp0iWkpKSgra0N06ZN45J6Ly8v6Ojo8B0qIWTooKSQENJ3ZWVliImJwcWLF5GcnIyrV6+is7MT5ubmcHFxgYuLC5ycnGBnZ0fDlshZcXExcnJykJGRgUuXLuHy5csoLy+HoqIi7Ozs4OrqCk9PTyxYsACmpqZ8h0sIGbooKSSE9F9DQwNSU1ORnJyMpKQkxMfH4+7duwAAfX192Nvbw87ODvb29pg2bRomTpwIY2NjnqMeXkQiEfLz83H16lVkZWUhJycH2dnZqK2tBQAYGBjA09MTbm5ucHFxwcyZM6GhocFz1ISQYSRZie8ICCHDn6amJqZMmYL4+HikpaVBLBbjiSeewCuvvILCwkJkZ2fjypUr+PHHH1FXVwcA0NLSwoQJEzB+/HhMmDCBe25paQlzc3MIhUKe12pwtbS0oLS0FEVFRfjzzz9x48YN3Lhxg3ve0NAAANDR0cG0adNgb2+PoKAgjB07Fl999RWioqKQmpqKWbNmYcqUKZQQEkL6jI4UEkL6JS0tDXv37sWPP/4IFRUVPP/889iyZQtsbGx6LF9UVCST7HT9K018AGDMmDEwNTWFpaUlTE1NYWFhARMTExgaGsLAwIB76OvrD9kEsqWlBdXV1TKPyspKiEQilJSUoLy8HMXFxSgvL5cZQFxLS4tLlu//a2lp2eOySktLsW/fPuzevRt1dXVYtmwZQkNDMWfOnMFaXULI8EanjwkhfdfW1obff/8dYWFhSEhIgJOTEzZs2IC1a9f2q0eyNFkqKyvjkiXp69LSUohEIu60dFcaGhpckqiurg41NTXo6upCKBRCXV0dOjo6EAqF3NEzdXV1qKqqcp9XUFDo1umitrYWXTePra2taGpqAnCvN29LSwvEYjGamprQ0tKC2tpaNDc3o6mpiUsAGxsbu8VqYGAAY2NjWFhYyCS9lpaWMDMzg4WFRb9Orbe2tuLw4cP44osvkJmZyY05+Oyzz0JNTe2x50sIGfEoKSSEPLry8nIcPHgQu3btgkgkgr+/P0JDQ+Hj4zNoMXR2dqK6uhp3796VOQInfd3U1ITm5mYuSbv/OQDU1dWhs7OTm2d7e7vMUUrg3tE6JaX/XWGjqKgIbW1tAPeSSqFQCF1d3W7P1dTUuh3J7Pp6MO8Wk5aWhrCwMBw6dAh6enp44YUXsHHjRhp+hhDSE0oKCSEP1zW50NfXx7p16yi5GEZEIhF++OEHXpN5QsiQR0khIaRndBpy5JGe9t+7dy+io6MxefJk/OUvf0FwcDB1TCGEUFJICJFVWFiIvXv3Yv/+/aivr8eyZcuwZcsWuLu78x0aGUB97SBECBnxKCkkhNwTHx+PnTt3Ijw8HIaGhggJCcErr7wCQ0NDvkMjclRRUYHvvvsO//nPf1BSUoIFCxZg8+bNCAgIgEAg4Ds8QsjgoaSQkNGsvr4ev/zyC3bu3Inc3Fw4Oztj8+bNWLNmDZSVlfkOjwwiiUSCyMhI7Ny5EzExMZgwYQJeeuklvPzyy9DV1eU7PEKI/FFSSMhoVFBQgAMHDmDv3r1obm7GqlWrsG3bNjg4OPAdGhkCrl+/jq+//hoHDhyAgoIC1qxZg82bN2PatGl8h0YIkR9KCgkZLSQSCWJjYxEWFobIyEjY2NggJCQEwcHBMDAw4Ds8MgSJxWIcPnwYO3bswPXr1zFnzhyEhoYiMDAQioqKfIdHCBlYyYM3YBYhhBdisRhhYWGYMGECfH190dLSgsOHDyM/Px9vvfUWJYSkVzo6OggJCUFubi7OnTsHPT09BAUFYdKkSdi+fbvMXVgIIcMfHSkkZISiU4BEHnq69OD111+Ho6Mj36ERQvqHTh8TMpJQZwEyWKSdlHbt2oWcnBzqpETI8EdJISEjAQ0rQvh0/3BGzz33HDZt2gRzc3O+QyOEPDpKCgkZzmgAYjKUlJaWYt++ffjqq6+4gc9DQ0MxZ84cvkMjhDwcJYWEDDfSW5WFhYUhISEBTk5O2LBhA9auXQt1dXW+wyOEbpFIyPBESSEhw4VIJMIPP/yAXbt2QSQSwd/fH6GhofDx8eE7NEJ6lZaWhrCwMBw6dAh6enp44YUXsHHjRlhZWfEdGiFEFiWFhAx1XRtVfX19rFu3jhpVMuzQTg0hQx4lhYQMRXT6jYxU91/+MHnyZPzlL39BcHAwNDQ0+A6PkNGMkkJChpLCwkLs3bsX+/fv5y7U37JlC9zd3fkOjZABRx2lCBlSKCkkZCi4f0iPkJAQvPLKKzA0NOQ7NELkjoZUImRIoKSQEL5IB//duXMncnNzafBfMurR4OuE8IqSQkIGW0+3Cdu2bRscHBz4Do2QIYNu00jIoKOkkJDBIJFIEBsbi7CwMERGRsLGxgYhISEIDg6GgYEB3+ERMmSJxWJ8//33CAsLw61bt+Dt7Y2QkBAEBgZCUVGR7/AIGUmSFfiOgJCRTCwWIywsDBMmTICvry9aWlpw+PBh5Ofn46233qKEkJCH0NHRQWhoKG7cuIGoqCgIhUIEBQVh0qRJ2L59O6qrq/kOkZARg44UEiIHdOqLEPmhSzAIkQs6fUzIQKGL5AkZXNLOWrt27UJOTg511iKkfygpJKS/aDgNQvhHwzoR0m+UFBLyuGjgXUKGHhoAnpDHRkkhIX1x/y26nJycsGHDBqxduxbq6up8h0cI+f/oVpGE9BklhYQ8CpFIhB9++AG7du2CSCSCv78/QkND4ePjw3dohJCHSEtLQ1hYGA4dOgQ9PT288MIL2LhxI6ysrPgOjZChhJJCQh6EGhNCRg7auSPkgSgpJOR+dNqJkJGNLgMhpEeUFBIiRReoEzL6UIcxQjiUFBJCQ1kQQmhoKUIoKSSjlHTQ2507dyI3N5cGvSWEAKBB6MmoRkkhGV3o9liEkEdFt6skowwlhWTkk0gkiI2NRVhYGCIjI2FjY4OQkBAEBwfDwMCA7/AIIUOcWCzG999/j7CwMNy6dQve3t4ICQlBYGAgFBUV+Q6PkIGSrMB3BITIi1gsRlhYGCZMmABfX1+0tLTg8OHDyM/Px1tvvUUJISHkkejo6CA0NBQ3btxAVFQUhEIhgoKCMGnSJGzfvh3V1dV8h0jIgKAjhWTEoVM+hBB5o0tRyAhEp4/JyEAXhxNC+ECd1sgIQkkhGd5oGAlCyFBBw1uRYY6SQjI0pKamYubMmY9cngacJYQMVQMxEH5lZSX09PSgpKQkx0gJkUEdTQi/GGN48803MW/ePIjF4geWbWtrw5EjR+Dh4YGZM2fi8uXL+PLLL1FWVoawsDBKCAkhQ8K4cePwr3/9C6Wlpdi3bx/y8/MxZ84czJw5k7sG8WHef/99LF++/JHKEjJQKCkkvOno6MC6devw+eefo6WlBd9//32P5UQiEbZv345x48ZhzZo10NPTw7lz55Ceno6QkBC6VykhZEhSVVXFc889h4yMDKSmpmLq1Kl49dVXYWNjg7fffhvFxcU9fq6urg7ffvstIiMjMX/+fNTU1Axy5GS0otPHhBetra0ICgrCyZMn0dnZCQCwtrZGYWEhFBTu7aukpaUhLCwMhw4dgp6eHl544QVs3LgRVlZWfIZOCCGPrby8HAcPHsSuXbsgEong7++P0NBQ+Pj4cGV27tyJ1157DZ2dnVBWVsbYsWMRGxsLCwsLHiMnowBdU0gGX01NDZ544gmkpqaio6ND5r0TJ06gpqYGX3zxBTIzM+Hs7IyQkBA8++yzUFNT4yliQggZWG1tbfj9998RFhaGhIQEODk5YcOGDXjmmWfg4OCAmzdvQto8Kysrw9DQELGxsbC1teU5cjKCUVJIBld5eTm8vb1x48YNtLe3y7ynqKgIbW1tNDU1ISgoCJs2bepT5xNCCBmOLl++jF27duHIkSNQVVVFXV1dtzJKSkpQV1fH6dOn+9RhhZA+oKSQDJ7r169jwYIFqKqq6pYQdpWUlARXV9dBjIwQQvh3584deHt7Iz8/v8dtpKKiIpSVlXHs2DH4+/vzECEZ4ZKpr/sgaG9vR0NDA1paWtDc3IzGxka0tbUBQI8XEHd2dva4pwjcu92S9Jo7KYFAwA3QrKKiAg0NDaipqUEoFEJLS2tIDGmQkpICPz8/1NfXdztl3JWysjJ+/fVXSgrJgLi/zonFYkgkEnR0dKC+vr7X8vdTUlKClpZWt+lCoZC7rEFbWxtKSkrQ1dXttTwhD9Lc3Ixr165BIpH0+H5nZycYYwgICMD+/fvxwgsvDHKEPWttbUVTUxMaGhrQ3t7OtWu91afa2lrcfzyqazvWlbQtk74vbeM0NDSgoqIinxUaxehI4UO0traisrISd+7cQVVVFWprayEWi1FTU8M9v/9vc3MzxGIxOjs7UVtby/cqAAB0dXWhqKgIHR0dqKmpQVdXFzo6Ot3+6unpcc8NDQ1hbGwMQ0PDflW+mJgYLF26FG1tbQ9MCKU0NTVRXl4OTU3Nx14mGf7a2tpQUVEhU/ekj5qaGq7OSR9isRitra2oqanpNekbbNKkUVNTE6qqqjJ1rGvd6/rawMAARkZGMDU1hYaGBt+rQAbR66+/jl27dj3wTIqUQCDAv//9b2zbtq3fy21uboZIJEJ5eTmqq6u5OiZt57r+rampQWNjI+rq6tDe3v7QocTkTVdXF8rKytDS0oKmpiZXv7r+7frcwMAApqamMDY2puvUuxu9p48bGxtx+/ZtFBcXo6SkBKWlpaisrIRIJMKdO3e45/cnddK9la7J0/0bd6FQKJOESY8aqKqqQl1dHerq6lBVVQVwLwHq6VZIurq63e7IIZFIeqyA0iORwP/2zJqamtDa2sodmautrYVEIkFtbS2XtHZtTO9Pdu//Wejp6cHY2BhGRkYwNjbmkkVzc3NYWlrC0tIS1tbW3YaH+emnn7Bu3Towxnrd++3J119/jb/85S+PXJ4MHy0tLVzdk9Y/6Y6XSCTi6t79R9EVFBS4Oqanp9ctodLV1YWqqqrMkbr765y2tjYUFRUB9FzHejvC19sRD+mREQAyO4LSpLTrkcrW1laZBrZr3ZM+WltbZeavrq7OJYjSnTQTExMYGxvDysoK1tbWsLS0hJ6eXr/+J4R/TU1NMDEx6fPOzDvvvIOPPvqoxzs4dXZ2oqysDLdv38atW7dQXFzMtXFlZWWorKxEaWlpt2UKhcJekyo9PT1oaGhAW1sbysrK0NHR4eqZ9OidtG71Vp96OoPVtR3rqq6uDp2dnVz719bWhsbGRpmj/+3t7airq0NDQ0O3BLbr8/vrl46ODkxNTWFkZAQzMzOufllYWGDs2LEYO3YsTE1NuW3GKDByk8LGxkYUFBSgoKAAf/75J4qLi1FUVISioiKUlJTg7t27XFkNDQ1YWlrKbHTvf25kZAQjIyNoa2vzuFaDRywWo6KiAhUVFVwjLX0tfV5ZWYni4mI0NjZynzMwMICFhQWsrKxQU1OD+Ph4APeuhVFUVIRAIOBO3/X00xMKhdDW1sb8+fNx6NChQVtfMnDa2tpQWFiIvLw8/Pnnn7h9+zaKioq4JLCiooIrq6amBktLS65+dU1+uj43NDQcFXWvubkZd+/e5Rpu6ZHSrsnynTt3uCM6UpqamrCysoKVlRUsLS1hZWWF8ePHY9KkSZg4ceKo+O6Gu3379iEkJKTbdAUFBSgqKkJBQYHbub7/jEtAQAACAwNx+/Zt3Lx5E7dv38bt27dRWlrK7bSoqKjA3NycO0pmZmbG1TnpjoaZmRkMDAxG7BG05uZmVFVVoaysDBUVFSgvL+fas9LSUlRUVKCsrAxlZWXcJV7KyspcnbK2tsbYsWMxYcIE2NrawtbWdqTVreGdFEokEhQWFiI3N5dLAKWPkpISAPeSEemG0traGhYWFjJHtiwsLGgvu5/u3r2LkpISLukuLi5GXFwccnNz0dHRgdbWVi4B1NHR4fbEbGxsMHnyZDg4OMDe3h4GBgZ0A/lhpLy8HDk5OSgoKEB+fj7y8vJQUFCA27dvc42Wubk5xo4dy9U5ab2TPh8zZgzPazF8NTU1yRxxLS4u5o4GSZ9LEwJjY2PY2tpySeLEiRMxbdo0jB8/fjQdBRnympubu52ulR5FLioqwp9//omSkhJUVFSgpqYGDQ0NXF1TUFCAra0txo4dC2tra1hbW3OJjI2NDUxMTLpdj056JpFIUF5ejlu3buHWrVsoKiriEu3bt2+jsLCQO+poZmaGyZMnw9bWFpMnT8bkyZNhb28PU1NTntfisQyfpFAsFiM7OxtXr15Fbm4u0tLSkJmZyR1u1tPTw7hx4zB16lRMmzYN48aNw7hx4zBlyhS64wXP2tto1bQAAAAgAElEQVTbUVxcjMLCQi6Jv3r1KgoLC3Hr1i1IJBKoqKhgwoQJcHZ2xrRp0zB16lS4uLjAyMiI7/BHvfb2duTn58vUvbS0NJSXlwP4X92TPqR1cNKkSdTZgkcdHR0oKiri6l3Xukf1bmgrKyvj6llaWhpSU1MhEokA9NzWTZ06FZMnT4ZEIqEd60FSVlbGtWNd27TCwkIA9/5PU6dOhbOzM/eYMmXKUE/Mh2ZS2NTUhNTUVCQnJyM5ORkpKSnckT8DAwM4OjrC3t4e9vb2cHR0xNSpUynxG6YaGxtx9epVZGZmIjs7G9nZ2cjMzORO71tZWWHmzJlwc3ODi4sLnJ2d6X8tR4wxXL9+HcnJyUhKSkJycjKuXbuGjo4OCIVCTJs2DY6OjtzRXUdHRxgYGPAdNumj5uZmmXqXlZWFjIwMmXo3e/ZsmXonFAp5jnpkqq2tRWJiIhISEhAfH48rV66goaEBSkpKmDJlCpycnDB9+nQ4OTnBycmJzmwNcXfv3kVGRgbS09O5v3l5eejo6ICWlhZmzJgBT09PuLu7w93dHTo6OnyH3NXQSApv376NP/74A5cuXUJSUhKys7PR0dEBU1NTuLq6wsXFBU5OTrC3t4eZmRnf4ZJBUFpaiqysLGRmZuLSpUtITk6GSCSCkpISHB0dud+Fl5cX3fauH5qampCQkIDExERuJ6y2thbq6upwdnaGm5sbnJ2dYW9vj0mTJtGpxhFOWu8yMjK430NFRQVUVFQwffp0uLq6wtXVFfPmzYOJiQnf4Q5L5eXliImJ4ZLAq1evQiKRYPLkyXB3d4erqyumT58OOzs7SsRHiObmZuTk5CA9PR3JyclITExEXl4eFBQUYGdnBw8PD8yZMwfe3t4wNjbmM1R+ksLq6mpuzyg6OhppaWlQUlLCpEmTuC9HejqDECnpKRXpxjQtLQ0tLS0YN24cfHx84OPjgwULFtCRqweQSCRIT09HdHQ0oqOjER8fj5aWFpiamsLZ2Zmrf7NmzeJ6yJPRraysTKbOpaamorW1Vabe+fr6DrUjHkNGR0cHMjMzERERgZMnT+LKlStQVFSEo6Mj5syZAw8PD8ybNw+GhoZ8h0oGkVgsRkpKCuLj47n6JW3PAgICsGTJEnh6eg72dnhwkkLGGFJSUhAeHo6oqChkZGRAIBBg5syZ8Pb2hre3N9zd3WmviPRJS0sLEhISEBMTg5iYGKSlpYExhunTp8PX1xeBgYFwdnbucaiG0eTu3bs4ceIEIiIiEBcXh5qaGpiamsLb25tr1M3NzfkOkwwTjY2N+OOPP7gdi5ycHCgpKcHV1RVPPPEEAgMDMWnSJL7D5FVNTQ3Cw8Nx/PhxxMbGorGxEba2tli0aBH8/Pzg5eVFl8EQGU1NTTh//jzOnDmDs2fPIj8/H1paWliwYAFWrFiBZcuW9Ti49wCTX1LY2dmJ+Ph4HDt2DOHh4SguLsa4ceOwePFieHt7Y968ebRnSQZUTU0Nzp8/j5iYGERGRuLWrVuwtrbGihUrEBgYiDlz5gz1i3wHTEVFBY4fP46jR48iLi4OCgoK8Pb2hq+vL3x8fOgoPBkwIpEIMTExiI6ORmRkJCorK2Fvb4+VK1di5cqVsLOz4zvEQVFXV4fff/8dv/76K6KioqCgoAA/Pz/4+/vDz88PY8eO5TtEMowUFhbi7NmzOH36NKKiogAAfn5+CAoKwtKlS+V1c4eBTwozMzOxf/9+HD58GJWVlZg2bRoCAwMRGBgIJyengVwUIQ+UlpbG7ZRcu3YNxsbGCAoKwvr160dkQ9XS0oKjR4/iwIEDuHDhAlRUVODv74+VK1ciICBgpI2nRYagzs5OXLhwAUePHkV4eDjKyspga2uL5557Di+88MJwHaajV4wxnD9/Hl9//TUiIiIgkUjg6+uLp556CsuWLaM6RwaEWCzG8ePHcfjwYURHR0NJSQnLli3Dhg0bMHfu3IFcVDLYAKivr2f79u1js2fPZgDYpEmT2EcffcSuX78+ELMnpN+uXr3KPvjgAzZ+/HgGgLm5ubEDBw6whoYGvkPrt6tXr7ItW7YwfX19pqSkxFasWMGOHDkyItaNDF+dnZ0sPj6ebd68menr6zNlZWW2YsUKdvr0adbZ2cl3eP1SV1fHdu/ezaZOncoAMHd3d3bgwAF29+5dvkMjI1xVVRX75ptvmIuLCwPA7O3t2Z49e1h9ff1AzD6pX0lhaWkp27x5M9PS0mJCoZA988wzLC4ujkkkkoEIjpABJ5FIWExMDFu9ejVTVVVl2trabOvWray8vJzv0Prs3LlzzMvLiwFgNjY27KOPPmJlZWV8h0VIN83Nzeynn35ic+fOZQDY2LFj2a5du1hLSwvfofVJVVUV27ZtG9PS0mLq6uosODiYXblyhe+wyCiVkpLC1q1bx4RCIdPR0WHvvPNOf3dMHi8pLC0tZZs2bWJCoZBZWlqyHTt2sOrq6v4EQsigq6ysZJ999hkzMzNjampqbMuWLcMiOYyNjWWenp4MAPPz82NnzpwZ9kdeyOhx7do19uqrrzKhUMgsLCzYf/7zH9ba2sp3WA/U0NDAPvzwQ6ajo8OMjIzYp59+SkcFyZBRVVXFPvnkE2ZgYMD09PTYJ598whobGx9nVn1LChsaGtjrr7/OJYO7d+8ednt6hNyvubmZ7dy5k5mbmzM1NTX25ptvPm6FkqucnBw2b948BoAtXLiQJSQk8B0SIY+tpKSEvfrqq0xVVZVZWVmxQ4cO8R1Sj3744QdmbGzMtLW12fvvvz9Qp+kIGXBisZi9++67TFNTk5mamrKff/65r7N49KQwOjqa2djYMH19/WGfDH766acMAAPAzM3N+Q6HDBHS5FBXV5dNmDCBnT9/nu+QGGOMtbe3sw8//JCpqqoyFxcXdvHiRb5D4t1orMPyXGc+v8/i4mIWHBzMBAIBW7FixZA5Wl9ZWckCAwOZgoIC27RpE6usrOQ7JEIeyZ07d9iGDRuYQCBgTz31VF/O5D48KWxoaOAq7JNPPslEIlH/oh1CHB0dR02DQh5daWkpW7ZsGRMIBGzjxo2submZt1hyc3PZjBkzmJqaGvv0009ZR0cHb7EMRaOxDstznfn8PmNiYpiNjQ0zMDBghw8f5iUGqbNnzzITExNmbW3N4uLieI2FkMd17tw5ZmFhwczMzFhMTMyjfCTpgYO2lZWVwcvLC8ePH8eRI0dw5MgRvm/BQojcmZmZ4fjx4/jvf/+LX375BfPnz0dFRcWgxxEdHQ13d3eoqKggIyMD27ZtG7K3mdPU1ISHhwffYZBhbMGCBcjKykJQUBBWr16Nf/zjH2A83IX122+/xeLFi7FgwQJkZmZi3rx5gx7DQKK6OXr5+PggKysLHh4eWLRoEX766aeHfqbXpPDOnTtYsGABGhsbcenSJaxcuXJAgyVkqFu9ejWSkpJQVVWFBQsWoKqqatCWHRMTgyVLliAgIADnz58f9XeIIKODpqYmdu/ejb179+KTTz7B22+/PajL//nnnxEcHIy//vWv+Pnnn+kGC2TY09PTw6FDh7B161Y8//zzOHLkyAPLK/U0sb29HcuXL4dEIsGFCxfoxudk1LK1tUVcXBzmzp2LwMBAxMbGQkmpx2ozYG7cuMEN+H7w4MFRcxcWQqSCg4MhFArx/PPPY+LEiQgODpb7MtPT0/Hiiy9i27ZteP/99+W+PEIGi0AgwPbt29HS0oLnnnsOkydPhr29fc+Fezqp/N577zFNTU127dq1AT2/3Rc1NTXchc/SxwcffMAYu3fhfdfpK1eu5D5XVVXFtm7dysaNG8eUlZWZrq4uW7RoEYuNje22jPuvn/nggw+4ec6ZM4ebfvr0aW66gYEBNz08PFwmjlu3brGnnnqKaWpqMn19fbZ27Vp29+5ddvPmTRYQEMA0NTWZiYkJCw4OZnV1dd3iqaioYJs2bWLW1tZMWVmZjRkzhq1YsYKlp6f3+fvjK7b29nZ26NAh5uPjw4yNjZlQKGR2dnbsyy+/lBk25f74bt68yZ566immo6PD9PX12eLFi9mNGzf6vN7ykpWVxdTU1Ni//vUvuS/L3d2dOTs7896Zq6Wlhb377rvM1taWqampMT09PRYQEMB+//137trGrh0Uuj4UFRVl5tWXetm1rIqKCjM3N2fe3t7su+++Y01NTTJl76/DP/74Y7dY+tpx4WHr3ddt00DXRek6X7t2jT3xxBNMW1ubqampsXnz5rH4+PgHfp993Sby7Z133mHq6urs9u3bcl1OZ2cnc3BwYPPnz+dteKf7fyfXr19nq1atYvr6+tw0aWeXR9keP6xu9re96ym+ffv2yWW7Tu3ZwOjo6GAeHh5s5syZvY0n3b2jSXV1NdPS0mKffPKJ/CN8BIsWLWIKCgo9fqFubm7sv//9L/e6vLyc2djYMGNjYxYREcHEYjHLy8tjgYGBTCAQsH379sl8vrcNoIaGhkwlkXJ2dpapJFLLli1jAFhgYCBLTU1lDQ0N7ODBgwwA8/f3Z8uWLWPp6emsvr6e7dmzhwFgW7dulZlHWVkZs7a2ZsbGxiwyMpLV19eznJwc5uXlxYRCIUtMTHzk74zP2CIiIhgA9vHHH7O7d++yyspKtnPnTqagoMC2bdvWa3zLli1jiYmJrKGhgZ07d46pqamxWbNmPdY6y8t7773HdHV1mVgsltsyIiMjmUAgYGlpaXJbxqMKDg5mOjo6LCoqijU1NTGRSMS2bdvGAHS7+L63OsNY3+qltKyJiQmLiIhgdXV1TCQScQ3Yjh07ZOZ9fx3u6Ohgr732Glu4cOFjjyP3qOvt5+f3wG3T/cNBDERdlK6zjo4Omz9/PouPj2f19fUsJSWFOTg4MBUVFZle8wO1TeRLW1sbGzduHHv55Zflupxjx44xBQUFdvXqVbku51FIfydeXl4sLi6ONTY2suTkZKaoqMgqKyv73FY8qG4+6P2HtXe9xde1zEBv16k967/MzEwmEAjYyZMne3q7e1L43XffMaFQ2GN2zYfo6GgGgG3cuFFmenx8PLOysmLt7e3ctHXr1jEA7JdffpEp29LSwg1Q3LX39EAnhZGRkTLTp02bxgCwP/74Q2a6jY0Ns7W1lZn2/PPPMwDdGpLy8nKmqqrKnJ2duy33UQx2bBEREWzevHnd4li7di1TVlbullBJ44uIiJCZ/uSTT8rsGQ8Fd+/eZcrKyjI7IgPtmWee6fH744ONjQ1zd3fvNn3SpEl9Sgr7Ui+lZXvqfbpo0aIHJoU1NTXMz8+PhYaG9quX9qOu99mzZ3vdNpmbm7O2tjaZ6QNRFxm7t84AWFJSksz0rKwsBoA5Ojpy0wZqm8inHTt2MD09vW7f50Bas2YN8/Hxkdv8+0L6Ozl16lSP7/e1rZBXUthbfF3LDPR2ndqzgTF37lz23HPP9fRW997HaWlpmDlzJrS0tO5/ixfe3t6YPn06vv/+e1RXV3PTP/30U2zZskXm+q7w8HAAwOLFi2XmoaqqCm9vbzQ3N+Ps2bNyi3XmzJkyr83MzHqcbm5ujrKyMplpx48fh4KCAgICAmSmm5iYYNq0aUhLS0NJScmQjy0gIABxcXHdlu/o6Ij29nbk5ub2GN+sWbNkXltaWgJAt1j4pKenh+nTpyM1NVVuy7hy5cqQ6e24aNEiJCYmIiQkBMnJyejs7AQA5OXl9SnGvtRLaVl/f/9u8zl9+jS2bNnS4zLy8vLg4uICBQUFfPnll/3qpf2o6+3r6wt7e/set02bNm2CsrJyj/PvT12UEgqFcHFxkZlmb28PMzMzZGZmory8HAD/28SBMG/ePNTU1KCwsFBuy0hPT4enp6fc5v84Zs+e3eN0ebcV/Y2vK3lt16k96x9PT0+kp6f3+F63pLCurg66urpyD6ovXn/9dTQ1NeE///kPACA/Px8XLlyQufi4tbUVYrEYQqGwx4RWOpSOSCSSW5za2toyrxUUFKCoqAh1dXWZ6YqKipBIJNxraewSiQQ6OjoQCAQyjytXrgAACgoKhnxsYrEY//jHP2Bvbw89PT2u3BtvvAEAaGpq6jG++3v5qaioAIBMLEOBrq4uxGKx3OYvFouHTI/H3bt34+DBgygsLIS3tze0tbWxaNEiLtF4FH2plw8r25uamhosX74cFhYWOH369CMNu/AgfVnvLVu2dNs2xcbGIiQkpNf5P25d7MrAwAACgaDbdCMjIwBARUXFkNgmDgQ9PT0AkGu9q6urGzL1TkpDQ6PbtMFoK/oT3/3ktV2n9qx/HtSOdUsKzczMcOvWLXnH1CdBQUGwtLTEV199hdbWVnz++edYv369zIZOVVUVOjo6aGlpQX19fbd53LlzBwAeqSe1goIC2trauk2vra3tx1r0TlVVFbq6ulBSUkJ7ezsYYz0+5s+fL5flD2RsS5YswQcffID169cjPz8fEokEjDHs2LEDAHgZd2wg3bx5E+bm5nKbv5mZGW7fvi23+feFQCDAs88+i+joaNTW1uL48eNgjCEwMBBffPFFt7I96Uu9fFjZ3igpKSE6Ohq///477O3tsX79eqSkpPRhTWX1Zb2feeYZGBsby2ybnn/+eS6RkZfeNujS8TSNjIwGdJvIp5s3bwKAXOudqanpkKl3D/I4bUVvdVNqsNs7PlF7ds/t27d7rU/dkkJvb2/k5OQgLy9P7oE9KiUlJYSGhqKiogKff/45Dh06hM2bN3crt2LFCgBAZGSkzPTW1lbExMRATU0Nfn5+D12eqakpSktLZaaJRCIUFRX1Yy0eLDAwEB0dHUhISOj23vbt22FlZYWOjg65Lf9BHjW2zs5OJCQkwMTEBJs3b4ahoSG3QWpubh7ssAdcVlYWCgoK4OPjI7dleHt748SJE9wpSz7p6uri+vXrAABlZWUsXLgQx48fh0Ag6FbH1NXVZRoWW1tb7N27F0Df6qW07KlTp7rFM336dGzdurXbdC0tLZibm0NTUxMnTpyApqYmli9fzp1Cled6q6qqYuPGjdy26eeff0ZoaOhjLbcvGhoakJmZKTMtOzsbZWVlcHR0hKmpKYCB2yby6dixY5g8ebJck8J58+YhMjJyyBzJeZC+thUPqpsAP+0dn0Z7e9bZ2YnIyMjeLwG6/yrDjo4ONmXKFPbkk08OxPWMA6auro7p6OgwgUDQ2wWS3Xra1dXVyfS027t3r0z53i6qfvXVVxkAtmvXLlZfX89u3LjBnnrqKWZubv7AC2/vvx2an59ft6E5GGPMy8uLaWhoyEy7c+cOGz9+PBs3bhw7deoUq62tZdXV1WzPnj1MXV39sW/7NNixLViwgAFg//73v1llZSVrampisbGxzMrKigFg586de6T43nrrLQbgsYbjkZeAgADm6Ogo1yErCgoKmJKSUrdeoXzQ0dFhXl5eLDMzk7W0tLA7d+6w9957jwFgH374oUzZRYsWMR0dHVZUVMQSExOZkpIS15OzL/VSWtbU1JSdPHmS1dXVseLiYrZhwwZmbGzcbWiSnurw+fPnmbKyMnN1dX2sYX36st6M3btHrpqaGhMIBGzZsmW9zncg6iJj99ZZQ0ODeXh4sOTkZNbQ0PDIvY8fd5vIl1u3bjE1NTUWFhYm1+Xk5OQwBQUF3m+vx1jvvxOpvrYVD6qbjA1ce/coZfq7Xaf2rP8OHjzIFBUVWV5eXk9v93zv4zNnzjCBQMC+/fZb+UbXR2+88QYDwDIzM3stU1VVxbZs2cJsbGyYsrIy09HRYX5+fjL3/etp/Ka//e1v3Pu1tbUsODiYmZqaMjU1Nebh4cFSUlKYs7MzV/6tt95iSUlJPc4nJSWl2/RPPvmEXbx4sdv0f/7zn9xyq6ur2WuvvcaNJ2ZoaMh8fX27/fAeBV+xVVZWspdffplZWloyZWVlZmxszNatW8fefvttbr7Ozs69xscY6zZ98eLFfV7/gbZnzx6moKDQ49huA23r1q1MW1u7t0o7aDIyMtjLL7/MpkyZwtTV1Zm+vj5zdXVl+/bt6zbG1fXr15mnpyfT0NBglpaWbPfu3TLvP0q97K2sqakpW716NcvPz+fK/PLLL91+Jzt27Ojxd/XMM8/Ibb2l1q9f32PvR8YGri523W6Zm5uzy5cvs/nz5zNNTU2mpqbGvLy8eh2nsL/bRD60trYyDw8PNnXq1EEZs/OFF15gRkZGfR7XcqD09Dvp4bgNY6xvbcXD6mZ/2rv745PXdp3as4FRUlLCxowZwzZs2NBbkSQBYz2fFH/nnXfw+eef4+jRo1iyZElPRQgZFX777TesWbMGf//73/HPf/5T7strbW2Fl5cX7ty5gwsXLnA918jQ9d1332H37t1y7Zk+mnR0dGDNmjWIiopCQkIC7Ozs5L7Muro6buSNuLi4bp0ZCBnOampqMG/ePLS3tyMlJaW3jkLJvd4/6+OPP8aLL76IwMBA7NmzR36REjKEffnll1i9ejVeeeWVQUkIgXvXqUVGRkJTUxNubm69Dh1Aho49e/bgtdde4zuMEUEsFmPx4sU4ffo0IiIiBiUhBO71aD179ixEIhHmz58/5HtlE/KoSktL4eXlhZqaGpw5c+aBPcd7TQoFAgG+/vprfPjhh3jllVewZMmSbhejEjJSiUQiBAYG4rXXXsPf//53fPnll4O6fAMDAyQmJsLBwQHu7u7Yvn37kOh8Qu7Zv38/VqxYgYaGBuzZswc1NTV46qmn+A5r2Lt48SJmzpyJ7OxsnD9/HnPnzh3U5dvY2CAhIQFNTU1wdHRERETEoC6fkIF29uxZzJo1Cx0dHYiPj4eVldWDP/Ao56EvXLjAJk2axHR1ddk333wzUKe3yWNAD9d03P/oeu0E6btff/2VGRgYsHHjxg3KNYQP0t7ezv71r38xoVDInJychsSFysPRQNcb6T1elZSUmIODw5C4LeFw1tjYyN566y2moKDAAgICWGlpKa/xiMViFhISwgCwZ599ltXX1/Maz0hF7Zn8NDU1sc2bNzOBQMBWrVrFampqHuVjPXc06UlDQwPbvHkzU1BQYHPnzuW9sSRkoEVFRbE5c+YwBQUFtnXrVtbY2Mh3SJzc3Fzm4uLCVFVV2auvvspKSkr4DomQfmtpaWG7du1i5ubmzNDQcEj0/u3q119/Zfr6+szGxoYdPHhQriMPEDIQOjo62HfffcesrKzYmDFj2LFjx/ry8UdPCqWSk5PZwoULGQDm6enZY+9BQoaTs2fPMnd3dwaA+fn5scuXL/MdUo86OjrY119/zSwtLZlQKGSbNm3i/YgKIY+jpaWFffXVV8zCwoIJhUK2efNmVlFRwXdYPSotLWUvvfQSU1JSYnZ2duz48eN8h0RINxKJhP32229sypQpTFlZmYWEhDxOT/q+J4VSCQkJzNfXlwFgs2fPZgcOHGANDQ2POztCBlVdXR3bu3cvmzlzJgPA/P39WVJSEt9hPZL7G9R169axxMREvsMi5KGKiorYP//5T2Zubj7sdmyuXbvGnnzySSYQCJizszP77rvvHjhWHyGDobGxke3fv585OjoyBQUFtmbNGpnhu/ro8ZNCqcTERLZmzRqmqqrKtLW12csvv8xSU1P7O1tC5OLSpUts/fr1TEtLiwmFQrZ27Vp26dIlvsN6LC0tLezrr79mjo6ODACzt7dnO3fufNRrRwgZFB0dHezEiRMsICCAKSoqMiMjI/bmm28O20sgUlNT2erVq5mKigobM2YMe/PNN9nNmzf5DouMMjdu3GCvv/4609PTY6qqqmzt2rUsIyOjv7PtfZzCvqqursbBgwexb98+XLt2Dfb29li5ciUCAwNhb28/EIsg5LFkZGQgPDwcR48eRW5uLuzs7LB+/Xo8++yzcr9H7WC5fPky9u7di0OHDkEikeCJJ57AypUrsXjxYhpvjQy6zs5OXLx4EUePHsWxY8dQXl4Ob29vhISEYNmyZVBRUeE7xH4TiUTYt28fvvnmG279goKCsGLFCujr6/MdHhmBqqqqcOzYMRw+fBjnz5+HhYUFXn75ZQQHB8PIyGggFpE8YElhV/Hx8Th06BDCw8NRVlaGCRMmYOXKlVixYgVmz5790Bt0E9IfEokEly5dwrFjx3Ds2DEUFhbCwsICK1aswJo1a+Dm5sZ3iHJTV1eHw4cP48iRI4iLi4OioiIWLlyIlStXYunSpdRYEblpb29HbGwsjh07hvDwcFRWVsLOzg4rV67E2rVrMWHCBL5DlIuOjg6cOHECP//8M06dOoXOzk4sXLgQQUFBWLZsGXR0dPgOkQxjtbW1CA8Px6+//oro6GioqKhg8eLFeOaZZxAQEABFRcWBXJx8ksKucnNzceTIERw6dAh5eXkYM2YM5s+fDx8fHyxcuBA2NjbyXDwZJcrKypCQkIDo6GicPHkSZWVlGDt2LJYuXYpVq1Zhzpw5o25npKamBhERETh58iROnTqFlpYWODk5wcfHBz4+PvD09ISqqirfYZJhrLCwENHR0YiOjsa5c+dQW1uLqVOnYtWqVQgKCsKUKVP4DnFQNTc3Izo6Gj/++CNOnDiBjo4OODk5ISAgAEuWLMGMGTNG3XaI9F1ubi5OnjyJ6OhoXLhwAQKBAAsXLsSqVauwYsUKaGlpyWvR8k8Ku8rMzERUVBSio6MRHx+PpqYmjB8/Ht7e3liwYAHc3NwePrAiIQBu376NxMRExMXFITo6Gjdv3oSGhgY8PT3h4+MDX19fumyhi7q6OkRFReHcuXOIjo5GYWEhNDQ0MHfuXC5BdHJygrKyMt+hkiHsxo0bSExMRExMDKKjo1FWVgY9PT1uR9/f3x9jx47lO8whoba2FqdOncKZM2cQFRWFO3fuwMTEBIsWLcLChQvh6elJt7AkAICioiJcuHABUVFR3G/F1NQUfn5+WLRoERYtWjRYR5wHNynsqrW1FUlJSYiOjkZMTAxSU1PR0dEBU1NTuLq6cg9nZ+cH3pKFjGbq1kAAACAASURBVHwNDQ1ITU1FcnIyLl26hOTkZIhEIigpKWHWrFnw8fGBt7c33NzcRsS1SoOh6xGe2NhYVFdXQ01NDTNmzICrqyvc3Nzg6uoKc3NzvkMlPKmvr0dKSgqSkpK4eldZWQlVVVW4ublxR5xnzpw50KewRhzGGNLT03H27FmcPXsWiYmJaG9vh6WlJTw8PODu7g4PDw/Y29vTdznCdXR0ICsrCwkJCdyjpKQEKioqcHd3x6JFi+Dn5wdHR0c+jirzlxTer7GxEWlpaUhOTuYa/7KyMigpKcHOzg6Ojo6wt7fn/hobG/MdMpEDkUiE7OxsZGZmIisrC5mZmcjNzUVnZyfMzc3h4uICNzc3uLi4wNnZGerq6nyHPOwxxpCXl8fVvaSkJO47t7S0hLOzMxwcHGBvbw8nJyeMGzcOCgq93iGTDEMVFRXIzMzk6l1GRgauXr3K/QakOwnSekeXHfRPU1MTUlJScPHiRSQmJiIxMRFisRhaWlqYNWsWpk+fDicnJ0yfPh22trZQUlLiO2TyGDo6OnDt2jVkZGQgPT0d6enpSE1NRUNDA/T09ODu7s7tEMyaNQtqamp8hzx0ksKeFBcXIzk5GSkpKcjMzER2djbKy8sBAMbGxnBwcICDgwOmTp2KSZMmYeLEiZQsDhMikQj5+fkoKCjA1atXuQSwsrISAGBmZsb9f2fPng0XFxdYWFjwHPXo0dDQgJSUFCQnJyMjIwOZmZm4ceMGOjs7oampCTs7O+7/I617VlZWlCwOcVVVVSgoKEBeXh6uXr2KjIwMZGdnQyQSAfhfvXN0dMTs2bPh6uoKMzMznqMe+SQSCXJzcxEfH4/U1FRkZGQgJycHbW1tUFNT43bInJycYGtri8mTJ9P/ZYgpKSlBXl4e8vLyuCQwJycHLS0tUFVVhZ2dHaZPn45Zs2Zhzpw5mDJlylDcXg7tpLAnVVVV3N5sdnY2srKycO3aNTQ1NQEAtLW1MXHiRO4xadIkjB8/HtbW1jAxMRmK/4QRSSKRoLy8HLdv38aNGzdQUFAg86ivrwcAaGhoYMqUKVyCIT0abGBgwPMakPs1NzcjNzcXGRkZyMrKQlZWFnJyclBdXQ0AUFVVxYQJE7gkUVr/rK2tYWZmRtcrDpI7d+6gqKjo/7F352FNnenfwL8hCRBAAii7bIqAK7hUZVGsKOAouIKtW21rq50utE5nase3rXO1M9NOp9PaTat2rK3aukzdtVpqXdhUUBAXQFECyCoEAmENPO8f/eXUCG6Q5GS5P9eVi3ByyLkT/Z7nzslZUFhYyDWA6tzJ5XIAgEQi0ciduhHs168fz9UTtfb2di5vFy5c4HJXV1cH4LexLiAgAEFBQQgKCkJgYCD8/f3h6+sLBwcHnqs3TbW1tZDJZLh27RoKCgpw9epV5Ofno6CggBvTHB0dMWLECI2tvYMHDzaW9Z/xNYX3Ulpa2qXxuHbtGgoLC9Ha2goAEIvF8PT0hJeXF3x8fODt7Y3+/ftzP11cXODs7Eyb6h+gvb0d1dXVqK6uRklJicatqKgIJSUlKCsrQ3t7O4Dfm4U7m3X1jfZZM361tbUoKCjgbnfmr7GxEQAgFArh7u7O5c7Ly4vLYf/+/eHq6gpnZ2djWXHyRp27srIylJSUQCaTQSaTcfkrLi5GS0sLgN/Wd76+vggICOAadfVPLy8vOgrWSFVWViIvL4/bKqW+X1RUhI6ODgCAVCqFj48PfHx84Ofnx9338vKCh4cHXFxcaP/ru7S2tqK6uhq3bt3SyFZRURGKioogk8mgUCgA/LY+8/Pz47baBgYGIjAwEIMHD4azszPPr6RXTKcpvJfOzk6UlpaiuLi4y8qzuLgYJSUl3KdnNWdnZ+7m7u4OZ2dnrmF0dHSEg4MDpFIpHBwcuJux7mPT0tKCuro61NfXo66ujrvJ5XJuAKqqqkJFRQV3//bt2xrP4eTkBC8vL3h7e3cZ8L29veHp6UlbaM2UemuxOmvqHKp/v/v/Ut++fbkG0c3NDS4uLl2yd/fNGAc3xhiXsztzp55WXl6OqqoqVFdXa9xXqVTcc9jY2Gg02d7e3tzv6mnUZJuP1tZW3Lx5k2tk1E2NTCbDzZs3UV5ejjuHe3XWXF1duUbR3d0d/fr1g4ODAxwdHTVuOjwNik4oFArI5XIuY+r7t2/f5jJVVlbGjW+1tbXc31pYWMDd3R2+vr7w9fXlmmr1fT8/P6Nc7zwE028KH0ZjYyNKS0u7NEDqFbL6fnV1NeRyObp7y6ytrblBytbWFra2trC0tIS9vT2EQiEcHR1hYWEBqVQKsVgMOzs77m8dHBy6fGqXSCSwtrbWmNbc3MxtBVBTDy53vpb29nbU19ejo6MDdXV1UKlUaGhoQFtbG5RKJRobG7km8O7nAwCBQABHR0euMXZxcYGbm5tGo6weqD09PTVeCyGPoqmpCSUlJRp5u7MZuvODyd0f3tRsbGy47EkkEvTp0wcikQiOjo4QCoWwt7eHpaUlbG1tNXIlEom6HejUmb1TfX09Ojs7Naa1tLSgubkZwG9X8FAoFFzG1Fm9M49tbW1c41dfX9/ta7Gzs4Ojo6NGxu68r26U3dzc6Kte8khaW1tx69YtVFRUoKqqCrdu3UJVVRUqKys1mqTa2lpuC/+d1OOYo6Mj7O3tYWNjAysrK9jb20MsFkMqlcLKygo2Njbc+Ad0P5ap83gnpVKJtrY2jWl3jnnqbKnnq6+vR3t7OxQKBZdF9bgml8u5raZ36tOnD5ycnDSaYFdXV7i4uGhM8/T0NNWm70GoKeyJ+vp6jS1rd99vbGxEU1MTWltboVAo0NHRwf0nvXPgAH4fTO7W0NCgsVUAQJdmUu3OQezOZlQkEsHBwYEb/NSBtbOz67K1UywWY8mSJWhsbMSBAwcwevRoHbxzhPRMfX095s+fj1OnTuGbb75BYGBgly1sdXV1aG5u5rKj/kCkUCjQ2tqKpqYmLpeAZlOndveHLLU7Bzm1O5vKuz/wqQdCdYOqztjdWzltbGzw7LPPQi6XY//+/Rg7dqyO3kFCHqy+vh6JiYlITU3Fjh07MGjQoG63tikUCq5hUygU3Acfdc7UH4YAaNxXuzOHaurx6U53jnnq++pmVJ03e3t7WFtbQyKRcGPanVs47/yddg17IGoKye8aGhqwYMECJCcn47///S+efPJJvksiBIWFhYiPj0ddXR327t2Lxx57jO+StKqxsRFPPPEETpw4ge3btyM+Pp7vkogZKi0tRVxcHCorK7F//36MGTOG75KI/mXQjl6E06dPH+zduxfPP/88Fi5ciDVr1vBdEjFzKSkp3EnJ09PTTa4hBH77ynjfvn1YsmQJ5syZg08//ZTvkoiZOXPmDMaMGYOOjg5kZGRQQ2jGhGto5Cd3sLCwwLRp0+Du7o4333wTV65cwYwZM2izO9G7TZs2Yf78+YiJicGBAwdMeh86CwsLTJ8+HVKpFCtXrkRtbS2io6PpAC2ic//73/8wa9YsjBs3DkeOHIGLiwvfJRH+lNIah3Tr+eefx6FDh/DTTz8hKioKVVVVfJdEzERHRwdWrVqF559/Hq+99hp27NhhNleuSUpKws6dO7Fx40bMmDGDO/cZIbqwdu1aJCYmYtGiRTh48CDs7e35LonwjPYpJPdVUFCAuLg4qFQqHDhwAEOGDOG7JGLC7tyvdePGjVi0aBHfJfEiIyMDM2fOhLu7Ow4ePEhX8yFapVKp8PLLL2Pjxo34z3/+g1deeYXvkohhoH0Kyf0FBAQgLS0NXl5eGD9+PA4dOsR3ScREFRYWYvz48Th//jxOnTpltg0hAIwfPx7p6elobW3F+PHjkZ2dzXdJxETI5XJER0dj69at2LNnDzWERAM1heSB+vbti6NHj2L27NmYOXMmPvvsM75LIibGHA4oeVQDBgxAamoq/P39MXHiRBw5coTvkoiRu3HjBsLDw5Gfn4+TJ08iLi6O75KIgaGmkDwUKysrbNmyBR999BFeffVVLF++vMt5FAnpiU2bNiEqKgqRkZFITU2Ft7c33yUZDCcnJxw9ehTx8fGIj4/H+vXr+S6JGKn09HTug1dGRgZGjRrFd0nEAFFTSB6Jekf4rVu3Yvr06fe8MgMhD2LOB5Q8CisrK3z33XdYvXo1XnjhBSQlJXW5ugoh97Nz505ERUVh9OjROH36NLy8vPguiRgoOtCE9Eh2djbi4+MhlUqxf/9++Pn58V0SMSJ0QEnPbN68GcuXL8esWbOwZcsWSCQSvksiBowxhn/9619488038fLLL+Pjjz+m0xyR+6ErmpCeKysrQ3x8PIqKivDjjz9i4sSJfJdEjICpX6FE13755RfMmzcPgwcPxr59++Ds7Mx3ScQAtba24rnnnsP27duxdu1avPjii3yXRAwfHX1Mes7DwwMnT57EhAkTEB0djW+//ZbvkoiBowNKei8qKgopKSkoKytDaGgoCgoK+C6JGJja2lrExMRg79692LdvHzWE5KFRU0h6xdbWFj/++CNWrVqFpUuXYtWqVbS/E+kWHVCiPUOHDkVGRgYcHR0RFhaG06dP810SMRDXr19HWFgYCgsLcerUKUyfPp3vkogRoaaQ9JpAIMCaNWuwadMmfPzxx5g/fz6ampr4LosYCDqgRDfc3Nxw4sQJhIeHY+rUqdi+fTvfJRGepaamIjQ0FA4ODsjMzERISAjfJREjQ00h0ZpnnnkGx48fx8mTJxEeHo7S0lK+SyI8a2howKxZs7B27Vp8++23eP/992lHdy1Sb6lfvnw5Fi1aBLqUvfn65ptvMHnyZERGRuL48eNwdXXluyRihGjtTLQqPDxc40oMWVlZfJdEeEJXKNEPoVCItWvX4uOPP8Z7772HZ599Fu3t7XyXRfSEMYY1a9bg6aefxooVK7Bz507aEk96jJpConUDBw5EamoqAgMDMWnSJOzbt4/vkoie0QEl+peUlISDBw9i165ddA5RM9Ha2opFixbh73//O9atW4e1a9fSlnjSK/S/h+iEo6Mjjh49iqeeegqzZ8+mr7XMCB1Qwp/Y2FicPn0aV69eRUREBIqLi/kuiehITU0NpkyZgiNHjuDYsWNYsWIF3yURE0BNIdEZkUiEzz//nPtaa9myZWhra+O7LKIjdECJYQgODkZGRgZEIhHtwmGiLl++jDFjxqCsrAypqal4/PHH+S6JmAhqConO3fm1VlRUFKqrq/kuiWgZHVBiWDw9PXHq1CmEhIQgMjISBw4c4LskoiXJycmIiIiAu7s70tPTMXjwYL5LIiaE1tpEL2JjY5GSkoKSkhKEhYUhLy+P75KIltABJYapT58+2L9/PxYvXozZs2fj888/57sk0ktff/01/vCHPyA6Ohq//PILXFxc+C6JmBhqConeDB8+HJmZmXBzc0NYWBiOHz/Od0mkl+iAEsMmEomwbt06fPTRR0hKSkJSUhKdXN4IqY8wfu6557By5Ur88MMPdN1rohPUFBK96tevH5KTk/GHP/wBMTEx+PLLL/kuifQQHVBiPJKSkvDDDz9gw4YNmDdvHp1c3ogolUrMmTMH77//PrZs2YL3338fAoGA77KIiaKmkOidlZUVvvvuO7z33nt46aWXaOuFkaEDSoxTQkICjh8/jpSUFDz++OOorKzkuyTyAOXl5Zg0aRJOnTqFo0ePYvHixXyXREycgDHG+C6CmK+dO3di6dKlmDRpEn744QfY29vzXRK5j4aGBixYsADJycnYuHEj7T9ohAoLCzF9+nS0tbXh0KFDdKCCgcrNzcWMGTNgaWmJgwcPIjAwkO+SiOnLoC2FhFeJiYk4fvw4zp8/j4iICMhkMr5LIvdAB5SYhoEDByItLQ39+/dHeHg4fv31V75LInc5evQoIiIi4OXlhfT0dGoIid5QU0h4N378eGRmZkIkEiE0NBRnz57luyRyFzqgxLQ4OTnh559/xrRp0xAbG4tvv/2W75LI/9mwYQNmzJiBOXPm4Pjx4+jXrx/fJREzQk0hMQj9+/fHqVOnMGbMGERGRmL79u18l0T+Dx1QYpqsrKywdetWvPnmm1i6dClWrVoF2puIP+p9dVesWIHVq1dj8+bNsLS05LssYmaEa+j6Y8RAWFpaIjExEUqlEn/605/AGENkZCQdaceTjo4O/PWvf8WqVavw+uuvY/369TRImRiBQIBJkybBy8sLq1evxuXLlxEXFweRSMR3aWalsbERiYmJ2LZtG7Zs2YKXX36Z75KIeSqlA02IQdq4cSNefPFFzJkzB5s3b6ZzcukZHVBifn7++WckJCRg2LBh2Lt3L31tqSdlZWWIi4uDTCbD3r17ERERwXdJxHxlUFNIDFZycjISEhIQFBSEvXv3wtXVle+SzEJhYSHi4+NRV1eHvXv30v6DZuTSpUuYPn06LC0tcfjwYQwaNIjvkkxaTk4O4uLiYG9vj4MHD8LX15fvkoh5o6OPieGaMmUKzp49C7lcjjFjxiA7O5vvkkweHVBi3oYNG4aMjAxIpVKEhoYiJSWF75JM1pEjRzBhwgQEBgYiJSWFGkJiEKgpJAZt0KBBSEtLg7+/PyZMmICDBw/yXZLJogNKCAC4u7vj5MmTCAsLw9SpU/HDDz/wXZLJWbt2LWbMmIHExEQcPnwYDg4OfJdECABqCokRcHJywtGjRzFv3jzMmjULH3zwAd8lmRS6Qgm5m62tLfbs2YNly5ZhwYIFoOMRtaOjowMvvfQSXnvtNbz11lvYtGkTxGIx32URwqFDzIhRsLS0xObNmxESEoKVK1fixo0b+Pzzz++5QlUoFHR1lP/T1NR0zybvzgNKvv32WzqghHCEQiE+++wz+Pv7Y+XKlbh16xbWrVt3zyOTKyoq4ObmpucqDUttbS2cnJy6fayhoQFPPvkkTpw4gR9//BGzZs3Sc3WEPBhtKSRGJSkpCbt27cLWrVsxffp01NXVdZnn0qVLGDlyJGpra3mo0LD88ssviI2NRVtbW5fH6Aol5GEkJSVh9+7d2L59O6ZPnw6FQtFlns8//xzTpk0z62uYy2QyDBs2DHl5eV0eKy0txcSJE3H+/HmcOHGCGkJiuBghRig7O5t5e3uzQYMGsfz8fG56ZWUl8/T0ZADYCy+8wGOF/Gtra2P+/v4MAFu6dKnGY6dPn2bOzs4sJCSEyWQyniokxuTMmTPM1dWVDR8+nBUXF3PTDxw4wCwsLBgAtmnTJh4r5NfcuXMZAObt7c2qq6u56RkZGdz7RlkjBi6dmkJitG7dusXGjBnD+vbty06cOMGam5vZ2LFjmVgsZgCYhYUFy87O5rtM3vzrX/9iQqGQAWACgYD9+9//ZowxtnHjRmZpacnmzZvHlEolz1USY3Ljxg02ePBg5uHhwbKystj58+eZRCJhFhYWTCAQMEdHR1ZXV8d3mXp3+vRpJhAIGAAmFovZ2LFjWUtLC9u9ezezsbFhMTExrL6+nu8yCXmQdDpPITFqSqUSixcvxuHDhzF+/HikpqZCpVIBAEQiEUaPHo309HSzuypKRUUFBg4ciKamJm6aQCBAYmIidu7cibfffhvvvPOO2b0vpPfkcjnmzJmDrKwsiMViKBQKjcytXLnSrA4G6+jowIgRI1BQUKDxPgwfPhzZ2dl48cUX8cknn0AoFPJcKSEPRCevJsavs7MT0dHROH78eJdrtwoEAmzduhULFizgqTp+JCYmYu/evWhvb+emCQQCWFpa4r333sPrr7/OY3XE2NXU1GDw4MGQy+VcI6QmEolw5coVsznx9RdffIFXXnmly/6UFhYWiI2NxaFDh3iqjJBHRk0hMX67d+9GYmJil4YQ+K0R6tu3LwoLC83maOTTp08jMjKy2/dDJBLB1dUV58+fh4uLCw/VEWOnUqkQGxuLU6dOaXzoUBOLxYiNjcX+/ft5qE6/5HI5/Pz8UF9f3+3jAoEAW7ZsweLFi/VcGSE9Qlc0IcYtKyvrvkfNMsZQV1eHf/zjH3qsij8qlQrPP/88LCy6j7ZKpUJVVRXi4+PR2tqq5+qIKXj55Zdx4sSJbhtCAGhvb8eBAwdw7NgxPVemf6tXr9bYReNujDE888wz+PXXX/VYFSE9R1sKidEqKSnByJEjIZfLH3gqDJFIhMuXLyMgIEBP1fHjo48+whtvvIGOjo77zicQCPDUU09h8+bNeqqMmIIPPvgAq1ateuB8QqEQgwYNwqVLl0x2X7rc3FyEhIQ8cN0jEAgglUpx7tw5+Pv766k6QnqEthQS41VQUIDAwEAwxmBpaXnfeQUCAV566SU9VcaPsrIyvPXWW/dtCAUCAUQiESwsLFBZWYny8nI9VkiMnb+/PyZPnsztn3ovHR0dKCgowMaNG/VYnX699NJL9214BQIBhEIhhEIhHn/8cTpvKjEKtKWQGL2SkhJs374dn3/+OUpLSyESibrs/K62f/9+xMXF6blC/XjiiSfw448/3nM/r/b2dvj7+2PhwoV45pln6NrGpMdu3bqFrVu34tNPP0VZWdk9M2dvb4+bN2/e8yofxmr37t1ISEjo9jF11gYOHIjnnnsOTz/9NO2/S4wFHWhCTEdnZyeOHz+Ob775Brt374ZKpQJjjPt6x8LCAp6enigoKIC1tTXP1WpXdweXiEQidHR0wNbWFgsWLMDixYsRERHBY5XE1Kgzt27dOuzbtw8CgUCjORSLxXjxxRfx8ccf81ildjU3N2PQoEEoLy/XWLeotwzGx8dj+fLliIqKolM+EWNDTSExTbW1tdi+fTs2bNiA3NxcWFpacpd6e/fdd/H//t//e+jnqq+vR3t7OxQKBVpaWtDc3MxNv3t/ora2NiiVyi7PYWlpCVtb2y7T7e3tIRQKIRKJ0KdPH0gkElhbW3PTH0Z7ezuGDRuG69evgzEGCwsLMMYQExODZ599FnFxcQ/8ep2Q3rp16xb++9//4quvvsKtW7e4LWZCoRCXL19GYGDgQz2Poeft3Xffxdtvvw3g962CY8eOxfLly5GYmAg7O7uHeh5CDBA1hcS0qVQqnDp1Cl9//TX279+PxsZGiEQi/OlPfwLw2ykl5HI56urqUFdXB7lcjpaWFiiVSiiVym6vGawvAoEADg4O3ABnb28PBwcHODo6avy8cOEC9uzZAwDw9vbGkiVLsGzZMvj4+PBWOzFPKpUKlZWV2LdvH7Zt24aMjAx0dnYiICAAs2fPNvq8CQQCrF27FiqVCra2tpg5cyaefvpphIeHQyKR8FY7IVpCTSExXhUVFSguLkZxcTFKSkpw69YtVFVVcQdQVFVVoaqqSuMrVfWBFlZWVggMDISjo2OXJksikcDGxga2trawtLSEVCqFSCSCVCqFlZUVbGxsAIB7/E4WFhaQSqVdam1qaupyChj16XKA37b2NTY2cvOprxJRV1fHPaZQKLiBVP3z9u3buHnzJgB0OcDE3t4eHh4ecHZ2hru7O9zc3ODi4gIvLy/4+vrC29sbnp6eEIvFvf/HICavJ3mzsLCAtbU12tvb4evrCx8fH6PO25UrV9Da2orOzs4u++5S3ogJoKaQGC6lUomCggLk5+fj+vXrkMlkKC4uhkwmg0wmQ0tLC4DfBgY3Nzd4eHhwK2IPDw+4uLjAzc2Nm+bm5sYNIOXl5ejXr5/Rr6ArKirg5OQES0tLNDU1oaamBmVlZaiqqkJFRQXKy8tRXV2NsrIyVFZWoqKiAqWlpdyAKRQK4eHhAR8fH27g8vPzQ2BgIIKCguDs7MzzKyT6osu8McZQXFxs1Fuv1VtBPT09AYDyRkwRNYWEf5WVlbh48SIKCgpw9epVbmAqKSkBYwxisZhbgfr4+MDb25vb6uDt7Y3+/fvTPnOPgDGGiooKjUFf/bOoqAg3b95EY2MjAMDJyQkBAQEYPHgwAgICEBgYiGHDhmHgwIH3PEE2MWyUN/2ivBEjQk0h0a+ysjJkZWVxtytXruDGjRsAAEdHRwwYMAADBgzAkCFDMHToUO4+7a+jX3K5HJcvX+b+fdT3ZTIZOjo6YGdnh8DAQAwZMgSjR4/G6NGjMWrUKO6rPmIYKG/GgfJGDAQ1hUR3ampqkJaWhrS0NKSmpuL8+fNQKpUQiUQICgpCSEgIgoODERISgpCQEPTr14/vkskDNDc349KlS8jOzuZuFy9e5A7gCQoKQlhYGMLDwxEWFkZXcNAjypvpobwRPaOmkGhPUVERfv31V6SkpCA9PR15eXkAgKCgIISHh2PcuHEICQnBsGHDTO48geass7MThYWFyM7Oxrlz55CWlobMzEy0trbC1dUVoaGhiIiIwIQJEzBmzBj6GkxLKG/mifJGdIiaQtJzTU1NSEtLQ3JyMpKTk5GVlQWxWIwRI0YgPDwcERERePzxx2mLhBlSqVTIyclBSkoKUlNTcfLkSVRVVaFv376YPHkypkyZgtjYWLqqyiOgvJF7obwRLaGmkDya0tJS7Nq1CwcPHkRKSgra29sRHByM6OhoREdHIyIiAlZWVnyXSQxQbm4ujh07hqNHj+L06dNoaWnBsGHDMG3aNCQkJOCxxx7ju0SDQ3kjPUV5Iz1ATSF5sLKyMuzevRs7d+5Eeno67O3tMX36dMTGxmLq1KlwdXXlu0RiZJqbm3Hq1CkcO3YM+/btQ2FhIfz8/JCYmIjExESMGjWK7xJ5Q3kj2kZ5Iw+JmkLSvdbWVvzvf//Dhg0bcPr0adjZ2SE+Ph6JiYmIjo6mrRNEq7KysrBz507s3LkTRUVF8Pf3x9NPP41ly5bBxcWF7/J0jvJG9Mnc80buiZpCokkmk+Grr77C119/jdraWsTHx2Px4sWIjY2lndWJXpw9exbff/89tmzZAqVSiXnz5uGFF15AREQE36VpHeWN8M2c8kYeiJpC8pvc3Fy888472L9/P1xcXPDcc8/h+eef587eT4i+NTc34/vvv8e6deuQmZmJ4OBgvP3225g9ezYEAgHfdo9UHgAAIABJREFU5fUK5Y0YGlPOG3loGXSsupkrLCzEokWLEBISgqKiInz//feQyWT429/+RgMU4ZVEIsEzzzyDc+fO4cyZMwgMDMS8efMwduxYHD16lO/yeoTyRgyVKeaNPDpqCs1UY2MjXn75ZQwePBiZmZn4/vvvkZWVhYSEBKO/HjAxPWPHjsWOHTtw/vx5uLq6IjY2Fo8//jh3bj5DR3kjxsTY80Z6jppCM5SSkoLg4GB8//33+PLLL3Hp0iUkJibq7CuCf//73xAIBBAIBOjfv3+vn2/Hjh0ICQmBRCLhnvfSpUtaqJQ8rMOHDyMgIAAikUivyw0JCcHBgweRmpqKxsZGjBo1Cp9++ikMeS8YyhvpCblcjvXr12Py5MlwcnKCRCLBoEGDsHDhQuTk5OilBmPMG+klRsyGSqVib7zxBhMKhWzGjBmsvLxcr8sPDg5mnp6evXqOlJQUJhAI2J///GfW0NDArl+/zvr3789yc3O1VCW5n+vXr7O4uDg2YsQIZm9vz4RCIW+1tLe3szVr1jCxWMyioqJYWVkZb7V0h/JGeuPZZ59lIpGIffLJJ6y8vJwplUp26tQpNmTIECYUCtmePXv0Wo+h541oRTo1hWZCqVSyuLg4ZmNjwzZt2sRLDdoYpJKSkhgAVlpa2uUxW1tbFh4e3qvnJ/f35JNPsn/+85+svb2deXp68toUqp07d44FBAQwX19fdvXqVb7LYYxR3kjvPfvss+z555/vMj07O5sBYIMGDeKhKsPMG9GadP1+90N40d7ejsTERGRkZOCXX37B+PHj+S6px0pKSgAAffv25bkS8/T1119DIpHwXYaGMWPGIC0tDfHx8YiKikJqaip8fX15q4fyRrRh06ZN3U4PDg6GRCJBYWEhGGN6PzLY0PJGtIv2KTQDb775Jk6cOIHDhw8b9QAFAB0dHXyXYNYMrSFU69u3L44cOQJXV1fEx8ejpaWFt1oob0SXlEolmpubMWzYMN5OFWNIeSNaxve2SqJbaWlpzMLCgm3ZsoXvUu75dVZVVRV7+eWXmY+PDxOLxaxfv35s9uzZ7MKFC9w8e/bsYQC63MaNG8c+/PDDbh971K82715GUVERS0xMZHZ2dszJyYktWrSI1dbWsps3b7IZM2YwOzs75ubmxpYtW8YUCoVWXldeXh5LSEhgTk5O3LTq6mrGGGNXr15lM2fOZPb29kwikbDHHnuMHThwgEVFRXHzPvvss4+0/N4wlK+P73Tz5k0mlUrZ6tWreVk+5e3hUd56ZvPmzQwA27Fjh1aftyf4zhvROtqn0NTFxsayiRMn8l0GY6z7QaqsrIz5+PgwV1dXdujQIdbQ0MAuXbrEIiMjmbW1NUtLS9OYf+bMmQwAa25u7vL82trHSb2MOXPmsMzMTNbY2Mi+/fZbBoBNmzaNzZw5k124cIE1NDSw9evXMwDstdde08rrioyMZL/++itTKpUsIyODCYVCVl1dza5du8YcHByYp6cnO3bsGPd8U6ZMYc7OzszKyqpXy+8JQ2wKGWPsww8/ZHZ2dqy2tlbvy6a8PTrK28OrqKhgrq6ubNmyZVp5Pm3gM29E66gpNGU1NTVMJBKxnTt38l0KY6z7Qeqpp55iANi2bds0ppeXlzMrKys2evRojen6HKQOHTqkMX3o0KEMADt58qTGdD8/PxYYGKgxraev6/Dhw93WlJCQwACw3bt3a0yvqqpiNjY2XQapR11+TxhqU1hfX8+srKzYd999p9flUt56hvL2cG7fvs1CQkLY/PnzmUql6vXzaQtfeSM6kU77FJqw3NxcqFQqTJw4ke9S7mnv3r2wsLDAjBkzNKa7ublh6NChyMrKQmlpKS+1jRkzRuN3Dw+Pbqd7enqirKxMY1pPX9fYsWO7reWnn34CAMTExGhMd3Z2RlBQUJf5Dfl91TV7e3uEhITgwoULel0u5a13KG/3plQqERMTgyFDhmDbtm0QCoU9fi5t4ytvRDfo6GMTplAoAPwWWkPU2tqK+vp6AIBUKr3nfNeuXdPKSXgf1d3vm4WFBYRCIWxsbDSmC4VCdHZ2cr/35nXZ2tp2ma+1tRUNDQ2wtraGnZ1dl8cdHR27zG/I76s+SKVS7j3QF8pb71DeuqdSqZCQkABPT09s2bLFoBpCNT7yRnSDmkITpv6kXVxcjMDAQJ6r6crKygoODg5obGxEc3Nzr6+OYSgXbdf267KyskKfPn3Q0NCAxsbGLgNVVVWVTpdvjGQymd6P/KW88cPU87Z8+XK0trZiz549Gs/t7++PrVu3GsQR7nzkjegGfX1swoKDg+Ho6Ij9+/fzXco9zZkzByqVCqmpqV0e++CDD+Dt7Q2VSvVQz2VjY4O2tjbu98DAQGzYsEFrtT4Kbb4uAJg2bRqA37/WUquoqEBBQYHOl29M8vPzkZ+fj0mTJul1uZQ3ypu287ZmzRpcvnwZ+/btg5WV1SP/vT7wlTeiI3zv1Uh0a+XKlczDw4M1NDTwXUq3O75XVlaygQMHsgEDBrDDhw+zuro6VlNTw9avX89sbGy6nHbhfju+x8bGMqlUyoqLi1laWhoTiUTsypUrj1znvZYRExPT7YEVkZGRzNbWVmevi7HfLi/n5OSkcTRkbm4ui42NZT4+Pl12fH/U5feEoR5osmDBAhYUFMTLzviUN8qbtvKmPvXM/W7p6emP/LzaxmfeiNbR0cemrrKykjk7O7OlS5fyVkN35zW787xWNTU1bOXKlWzAgAFMLBYzZ2dnFh0dzX7++WdunnudN+3OlWJeXh6bMGECs7W1ZV5eXuyLL754pDrT09O7rfPcuXNdpv/zn/9kp0+f7jL9nXfeeaTX1d0y7/VZLT8/n82aNYvZ29szGxsbFhYWxk6ePMkmTZrEbGxsusz/MMt/VAcOHLjnALVx48YeP6+2/O9//2MCgYDt37+fl+VT3h4e5e3+pk+fbvBNId95I1qXLmCMsUffvkiMycGDBzFr1iz87W9/w+rVq/kuh2hZUFAQmpubIZPJ+C6FV2lpaYiOjsaSJUvw5Zdf8lYH5c20Ud5+Yyh5I1qVQfsUmoEZM2bg888/x1tvvYU333wT9DnA+FRUVMDJyQnt7e0a04uKilBYWIjJkyfzVJlhOHLkCGJiYjB16lR8+umnvNZCeTN+lLf7M6S8ES3jd0sl0afvvvuOWVpasilTprDi4mK+yyGPoLy8nAFgTz/9NCsuLmZKpZKdOXOGjR07ljk5ObHCwkK+S+RFW1sbe+edd5hQKGRPPfUUa2tr47skDuXNeFHeumfIeSNaQSevNieLFi3CuXPnUFVVheHDh/N2pKC+CQSCB97WrFnDd5n35ebmhuTkZNTV1WHixIlwdHREfHw8Bg0ahLNnz2LAgAE9el5jfm8uX76McePG4cMPP8RHH32EzZs3QywW810Wh/JmfP+n1ChvXRl63oiW8N2WEv1rampir7zyChMIBGzq1Kns7NmzfJdEyEOrqKhgr7zyCrO0tGQREREGv9WG8kaMmbHljfQKbSk0RxKJBGvXrsXJkyfR2NiIcePGYc6cObh8+TLfpRFyT3K5HH/9618xcOBA7N69G5988glOnDjR4602+kJ5I8bIWPNGeoeaQjM2YcIEpKWlYf/+/bhx4wZGjBiBuXPnIjk5mXaOJwajoKAAK1euhJ+fHzZu3Ig1a9bg+vXreOGFFwzykl/3QnkjxsBU8kZ6hppCghkzZuD8+fP44YcfUFVVhalTp2Lw4MFYu3Yt6urq+C6PmKGOjg7s27cPMTExCAoKwp49e7Bq1SrcuHEDr7/+OiQSCd8l9hjljRgaU84beTR0nkLSxcWLF7Fu3Tps3boVnZ2diIuLQ2JiIqZNm0YrB6IzjDGcOXMGO3fuxK5du1BWVobo6Gj88Y9/xPTp02FhYZqfYSlvhA/mmjdyXxnUFJJ7UigU2L59O3bs2IFTp07B1taWG7BiYmJgbW3Nd4nEBJw7d44bmGQyGQICApCYmIilS5di4MCBfJenN5Q3og+UN3If1BSSh1NTU4NDhw5h165d+OmnnyAWixEeHo4pU6ZgypQpGDVqFAQCAd9lEiNQU1OD48ePIzk5GUeOHEFJSQl8fHwwc+ZMJCQkICIigu8SeUd5I9pCeSOPgJpC8ujKyspw6NAhHD16FL/88gvq6urg4+ODmJgYTJkyBREREXB3d+e7TGIglEolzpw5g+PHj+PYsWPIysqChYUFwsLCEBMTg2nTpmHkyJF8l2mwKG/kUVDeSC9QU0h6R6VS4ezZs/jpp59w7NgxZGZmoqOjA35+fggPD0doaCgiIiIwdOhQOnLNTJSUlCA1NRVpaWlIS0tDTk4OVCoV/Pz8EBMTg5iYGEyePBn29vZ8l2p0KG/kbpQ3okXUFBLtamhoQEZGBreCysjIgEKhgL29PcaMGYORI0ciODgYISEhGDx4MEQiEd8lk14oLi5GdnY2d8vMzERJSQlEIhFCQkIQFhaGsLAwhIeHo3///nyXa3Iob+aF8kZ0jJpColsdHR24fPkyUlJSkJWVhezsbFy6dAltbW2wtrbG0KFDuYErMDAQgYGB8PLyov2lDExtbS0KCgqQl5eHS5cuITs7GxcuXEBtbS0EAgEGDBiAkSNHYuTIkQgLC8Njjz0GW1tbvss2O5Q300B5IzyhppDoX3t7O65evarxiffixYuoqakBANjY2CAgIIAbtIKCguDv7w8fHx+4uLjwXL3pamxshEwmw/Xr15Gfn4+CggLk5+cjLy8Pt2/fBgBYW1tjyJAhCAkJ4W7BwcH01ZQBo7wZJsobMUDUFBLDcfv2beTn53M39SflwsJCtLe3A/jtkmG+vr7w8fGBt7c3fHx8uJubmxvc3d3pE3M32traUF1djfLycpSUlEAmk6GoqAgymQzFxcWQyWRckwAAXl5eCAwMREBAAIKCgrj73t7edP4yE0F50x3KGzFS1BQSw6dSqTRWpkVFRSgqKuJ+Lykp4QYxALC1tYW7uztcXV3h4uICDw8PuLi4wNnZGX379oWDgwMcHR01fhrbvlb19fWQy+WQy+Woq6vjflZXV6OiogLV1dUoKytDZWUlqqqquC0Pau7u7hoDvHrA9/Pzg6+vLw30Zozy1hXljZgJagqJ8evs7ER5eTnKy8s1VtBVVVWorKxEeXk5qqqqUF1djdra2m6vM9unTx9uwLKysoJUKoWlpSVsbW1hY2PDTROJRJBKpdzfOTg4dNkfq0+fPl0Gvbq6ui7LbWhogEqlAgA0NTWhtbUVCoUC7e3tqK+vR1tbG5RKJffYnQPSvV5D37594e7uDhcXF7i5uXUZqF1dXdG/f386ETLpMcrb78ulvBETQ00hMT/qT/3qFf7dn/7b2tq4n0qlEkqlEm1tbaivr0d7ezsUCgWA33bqV9+/U3cDUncDl0Qi4QYL9f0+ffpALBbDwcGBGyRtbW1haWnZZWuLsW99IeaB8kaI0aCmkBBd6OjogEgkwu7duzF37ly+yyHEpFHeCNGKDNqDlRBCCCGEgJpCQgghhBBCTSEhhBBCCKGmkBBCCCGEgJpCQgghhBACagoJIYQQQgioKSSEEEIIIaCmkBBCCCGEgJpCQgghhBACagoJIYQQQgioKSSEEEIIIaCmkBBCCCGEgJpCQgghhBACagoJIYQQQgioKSSEEEIIIaCmkBBCCCGEgJpCQgghhBACagoJIYQQQgioKSSEEEIIIaCmkBBCCCGEgJpCQgghhBACagoJIYQQQgioKSSEEEIIIaCmkBBCCCGEgJpCQgghhBACagoJIYQQQgioKSSEEEIIIaCmkBBCCCGEgJpCQgghhBACagoJIYQQQgioKSSEEEIIIaCmkBBCCCGEABAwxhjfRRBi7BYtWoScnByNaQUFBfDw8ICdnR03TSwW4/Dhw3Bzc9N3iYSYDMobITqRIeK7AkJMQWBgILZt29ZlelFRkcbvw4cPpwGKkF6ivBGiG/T1MSFasGDBAggEgvvOIxaL8dRTT+mpIkJMF+WNEN2gr48J0ZJRo0YhOzsb94qUQCBAcXEx+vfvr+fKCDE9lDdCtC6DthQSoiVLliyBUCjs9jELCwuEhYXRAEWIllDeCNE+agoJ0ZL58+ejs7Oz28cEAgGWLFmi54oIMV2UN0K0j5pCQrTE3d0dERER99x6MXfuXD1XRIjporwRon3UFBKiRYsXL+4yTSgUYurUqejbty8PFRFiuihvhGgXNYWEaNG8efNgYaEZK8ZYt4MXIaR3KG+EaBc1hYRokYODA2JjYyES/X4KULFYjPj4eB6rIsQ0Ud4I0S5qCgnRsoULF6KjowMAIBKJMGvWLI2rLBBCtIfyRoj2UFNIiJbFxcXB2toaANDR0YGFCxfyXBEhpovyRoj2UFNIiJbZ2Nhgzpw5AIA+ffogJiaG54oIMV2UN0K0h659TMh9dHZ2or6+Hm1tbVAqlVAqlWhrawMAyOXyLvO3traiqakJ3t7eAICxY8di3759EIvF3X6lZWtrC0tLSwgEAjg4OHDz2djYwMrKSrcvjhADQ3kjhF90mTti0hobG1FeXo6qqirU1NSgrq5O4yaXyzV+VygUaGpqQmtrK+rr6+95clx9sbe3h0gkgoODAyQSCRwcHDRujo6OGr87OTnBxcUFLi4ucHZ25rV2Yn4ob4QYtQxqColRUiqVkMlkKC4uRnFxMcrKylBZWYny8nJUV1ejoqICFRUVaGpq0vi7u1f0d6/k7e3tua0JUqkUIpEIUqkUlpaWsLW1hUQi4fZf6tOnj8ZRj8Bv50izt7cHALz77rv461//CqFQiObmZrS0tHR5HXV1dWCMoaOjAwqFgttCoh4oFQoFOjo6IJfL0dzcfN9BVqFQaDy3WCyGs7MzXF1d4e7uDhcXF7i5ucHNzQ3e3t7cjQYz8iCUN8obMQvUFBLD1N7ejps3byI/Px/Xrl2DTCbTGJRqamq4ee3t7dG/f384OzvD09PznitmJycnboDRB5VK1WUQ06XOzk7U1NSgqqpKY8AuLy9HZWUlqqqquMG8oqKC+zuJRAJfX1+Ngcvf3x8BAQEICAigIznNAOXt0VHeiAmippDwq66uDrm5ucjPz0dBQQHy8/ORl5eHmzdvor29HQDg4eEBPz8/eHl5cStRHx8f7r6DgwPPr8L4tLa2cgN+cXGxRgNQXFyMoqIi7v3v378/N2AFBgYiMDAQQ4cO5fbjIsaD8sYPyhsxEtQUEv0pKytDVlYWrly5gsuXLyMrKwt5eXno7OyElZUVBg4ciKFDh2LAgAEYMGAAhgwZghEjRnBfDxH9UalUKC4uxo0bN3Djxg1cvnwZV65cwY0bN3Dz5k0wxiCVSjFs2DAMHToUQ4YMwejRozF69GhIJBK+yyegvBkTyhsxENQUEt2ora1Feno60tPTkZqaivPnz0OhUMDCwgL+/v4IDg5GSEgIgoODMWLECHh5efFdMnlI9fX1uHjxInJycpCTk4Ps7GxcvnwZzc3NEIvFGDp0KMLCwhAaGorQ0FAMHDiQ75JNHuXNdFHeiB5RU0i0o7CwECdOnEBaWhrS09ORl5cHxhiCgoIwfvx4jB8/HsHBwRg+fDhsbW35LpdomUqlQkFBAXJycpCZmYn09HRkZWWhra0Nrq6uCA0NRXh4OCIiIvDYY49BKBTyXbJRo7yZN8ob0RFqCknPNDY2IiMjA8nJyThw4ACuXLkCGxsbjBw5EqNHj0ZERAQmTZpER9qZMZVKhZycHKSkpCArKwsnT55EcXEx7OzsMH78eEyZMgVxcXEYMmQI36UaPMobeRDKG9ECagrJw8vLy8Pu3btx6NAhnDt3DgAwatQoREdHY+rUqQgNDYWlpSXPVRJDdvXqVRw7dgw///wzTpw4AaVSCX9/f8TExGDu3LmYOHEibdX4P5Q30luUN/KIqCkk95eXl4ddu3Zh165dyM3NhaurK+Li4hAdHY3Jkyejb9++fJdIjFRbWxvS0tLw888/4/Dhw8jOzoarqytmz56NhIQEREZGmt2ARXkjukJ5Iw+BmkLSVW1tLbZs2YLNmzcjNzcXbm5umDt3LubNm4cJEybQioPoxLVr17iGKDs7Gy4uLnjyySexfPlyDB48mO/ydIbyRvhgrnkj90VNIfldRkYG1q9fj507d0IsFmPBggV44oknMGHCBFhYWPBdHjEj165dw86dO7F582bcuHEDkZGRWLFiBWbPnm0yX5lS3oihMIe8kYdCTaG5Y4xhz549eO+993DhwgWMHDkSy5cvx8KFC+nM+oR3nZ2d+Pnnn7F+/XocOHAA/fr1Q1JSEl5++WWj/P9JeSOGzNTyRh4ZNYXm7PDhw3j77bdx4cIFzJs3DytXrsS4ceP4LouQbpWWlmL9+vX47LPPYG1tjVWrVmHFihVGc/JeyhsxJsaeN9IjGWDE7Fy8eJGFh4czgUDA4uPjWU5ODt8lEfLQbt++zf7yl78wW1tb5unpyb777ju+S7ovyhsxZsaWN9Ir6bTjihlRqVT4xz/+gTFjxgAAzpw5g3379mHEiBE8V0aM0eHDhxEQEACRSKTX5fbt2xcffPABCgsLMXPmTDz11FOYPXs2Kisr9VrHg1DeSG/J5XKsX78ekydPhpOTEyQSCQYNGoSFCxciJydHLzUYS96IlvDdlhL9uHbtGhs7diyTSCTso48+Yh0dHXyXRIzU9evXWVxcHBsxYgSzt7dnQqGQ13pOnDjB/Pz8WL9+/diePXt4rUWN8ka04dlnn2UikYh98sknrLy8nCmVSnbq1Ck2ZMgQJhQKefn/boh5I1pDWwrNQVpaGkJDQ9HZ2Ynz589j5cqVJnV0o52dHSIiIsx2+fr21ltvISwsDFlZWejTpw/f5SAyMhI5OTmYNWsW5syZg3//+9+81kN5M+3l69szzzyDpKQkuLm5wcbGBhMmTMD27dvR0dGBv/zlL3qvx9DyRrRLv9/7EL3LzMxEbGwsJk+ejO3bt8PGxobvkoiR+/rrrw1uZ/M+ffpg48aNGDZsGFauXAnGGP785z/rvQ7KG9GmTZs2dTs9ODgYEokEhYWFYIxBIBDotS5DyRvRPmoKTVhVVRXi4uIQERGBXbt2QSwW810SMQGG1hDeKSkpCRYWFkhKSkJQUBDi4uL0tmzKG9EXpVKJ5uZmjBgxQu8N4Z34zBvREZ6/vyY6lJCQwAYMGMDq6ur4LoXdvn2bvfbaa2zAgAFMLBYzBwcHFhsby44fP87N8+677zIADAALDw/nph85coSb3rdvX276hx9+yE2/86bex+3Oxz09PdnZs2fZ5MmTmZ2dHZNIJGzSpEksJSVFZ8vvzXtkaWnJPD09WVRUFNu8eTNramri5mtpaWFvvfUWCwwMZBKJhDk6OrIZM2awffv2MZVKxeRyeZea3n33XcYYY+3t7RrT586d26Na1Tw9PXnfp7A7Tz/9NHNxcWH19fV6WybljfKm67ypbd68mQFgO3bs0Mrz9RYfeSM6kU5NoYm6ePEiEwgEbP/+/XyXwsrLy5mfnx9zdXVlBw4cYPX19Sw/P5/NmTOHCQQCtnHjRo35bW1tNQYJtdGjR2sMEg+aXy04OJjZ2tqy0NBQlpaWxhobG9m5c+fYiBEjmKWlJTtx4oROl/8w1O+Rm5sbO3DgAFMoFKyiooIbOD/++GNu3mXLljGpVMqOHTvGmpqaWEVFBXv99dcZAPbrr79y88XGxjILCwt2/fr1LssLDQ1l27dv71XNjBluUyiXy5mDgwP7+9//rpflUd5+R3nTXd4YY6yiooK5urqyZcuWaeX5tEHfeSM6Q02hqVq9ejXz8/NjnZ2dfJfCli5dygCw77//XmN6S0sL8/DwYBKJhFVUVHDTdTFIAWAXLlzQmH7x4kUGgAUHBz/U8+lykFK/R9198o+NjdUYpPz8/FhYWFiX+QICAjQGqeTkZAaA/fGPf9SYLyUlhXl7e7P29vZe1cyY4TaFjDGWlJTEhg8frpdlUd5+R3nTXd5u377NQkJC2Pz585lKper182mTPvNGdIaOPjZVFy5cwMSJE3nd30Rtz549AIDp06drTLeyskJUVBSam5tx9OhRndZga2uLkJAQjWnDhw+Hh4cHcnJyUF5ertPlP4j6PZo2bVqXx44cOYJXX32V+z02NhZpaWl4/vnnkZGRgY6ODgBAfn4+Jk2axM0XFRWFkSNH4ptvvkFNTQ03/cMPP8Srr76q9/ML6ltkZCQuXbqE1tZWnS+L8qaJ8qb9vCmVSsTExGDIkCHYtm0bhEJhr55P2/SZN6I71BSaKIVCAalUyncZaG1tRX19Paytrbs9fYmrqysAoKKiQqd1ODg4dDvdxcUFwG8HCfDlQe/R3b744gt8++23uHHjBqKiomBvb4/Y2FhuoLvTn/70JzQ1NeHLL78EABQUFODUqVNYtmyZ1l+HoXFwcABjDA0NDTpfFuVNE+VNu3lTqVRISEiAp6cntmzZYnANIaDfvBHdoabQRLm7u0Mmk/FdBqysrCCVStHS0tLtykJ9Vnw3NzdumoWFBdra2rrMW1dX1+0yHmbrTE1NDVg3l/lWD07qwUpXy7+fB71H3S1v8eLFSE5ORl1dHfbu3QvGGObMmYP//Oc/GvPOnz8fXl5e+Pzzz9Ha2oqPPvoIzz33nEGcX1DXioqKYG1tDScnJ50vi/KmifKm3bwtX74cra2t2Llzp8YWR39/f2RkZPTqubVFn3kjukNNoYmaPHkyjh8/bhCf2mbPng0AOHTokMb01tZW/PLLL5BIJIiJieGmu7u749atWxrzVlRUoLi4uNvnt7Gx0RhUAgMDsWHDBo15WlpacO7cOY1pubm5KCsrQ3BwMNzd3XW6/AdRv0eHDx/u8tjIkSPx2muvcb87ODggLy8PACAWizF16lTs3bsXAoGgy3ssEomQlJSEqqoqfPTRR/jhhx/wyiuvPFJtxmre6UvOAAAJeUlEQVTv3r2YNGmSXk4cTXmjvAG6yduaNWtw+fJl7Nu3D1ZWVr16Ll3SZ96IDvG5RyPRndraWiaVStmaNWv4LqXL0ZAKhULjaMgNGzZozP/SSy8xAOyzzz5jDQ0N7Pr16ywxMZF5enp2u+N5bGwsk0qlrLi4mKWlpTGRSMSuXLnCPR4cHMykUimLiop6qKMhtb38R3mP3N3d2cGDB5lCoWAlJSXshRdeYK6urkwmk3HzSqVSFhkZyXJyclhLSwurrKxka9asYQDYe++91+W5FQoFk0qlTCAQsCVLljxSXQ9iqAeaZGZmMoFAoLfLcFHeKG9q2syb+tQz97ulp6f3ahnaoO+8EZ2ho49N2YcffsgsLS3Z2bNn+S6F3b59m7366qvMz8+PicViJpVKWUxMDPvll1+6zFtXV8eWLVvG3N3dmUQiYREREezcuXNs9OjR3IrwjTfe4ObPy8tjEyZMYLa2tszLy4t98cUXGs8XHBzMPD092ZUrV1hMTAzr06cPk0gkLDIyUuO8abpafk/fI3d3d/bEE0+wgoICjfmys7PZ8uXL2eDBg5mNjQ1zcnJi48ePZxs3brzn0a9//vOfGQCWk5PTo9rudODAgXsOUHef7oQPjY2NbMiQISwyMlKvRwNT3n5DedNe3qZPn27wTSFfeSM6kS5grJsdP4hJ6OzsxPTp03HhwgWcOnUKAQEBfJfEi5CQENy+fRulpaV8l0J0rLW1FXFxccjJyUFmZia8vLz0tmzK228ob+aDz7wRncigL/9NmIWFBXbt2gU/Pz9MmDABZ86c4bskQnSmtrYW0dHROHfuHH766Se9D1CUN2JO+M4b0Q1qCk2cnZ0dkpOTMW7cOEyYMAGrVq1Ce3s732URolXJyckICQlBYWEhfv31V4wcOZKXOihvxBwYSt6IDvD9BTbRj87OTvbVV18xGxsbNm7cOHb16lW+S9K57q6Vunr1ar0t/+5ld3d755139FaPsdfZnaamJvbGG28wCwsLlpCQwKqrq/kuiTFGeaO8GX+d3THUvBGtoX0Kzc2VK1fw1FNP4fLly/jjH/+IN954A87OznyXRcgjaW9vx5YtW/Duu+9CqVTiiy++wPz58/kuqwvKGzEFxpI30mu0T6G5GTJkCNLT0/H+++9j27ZtGDBgAFavXg25XM53aYQ8UEdHB7777jsMHjwYL774IqZPn45Lly4Z7ABFeSPGzNjyRnqPthSasaamJnzxxRf417/+hfb2dixduhQrVqxAUFAQ36URokEul+Obb77BunXrcPPmTSxZsgRvvfUWfH19+S7toVHeiLEwhbyRHsmgppCgoaEB69atw1dffYWbN28iMjISK1aswOzZs2Fpacl3ecSMnTlzBuvXr8eOHTsgFouxcOFCvPbaaxg0aBDfpfUY5Y0YKlPMG3kk1BSS33V2duLYsWNYv349Dh48iL59+2Lu3LmYN28eIiMjDfIi7MT05OXlYdeuXdi1axdyc3MREhKCFStWYOHChbCzs+O7PK2hvBFDYC55Iw+FmkLSvdLSUmzZsgW7d+9GdnY2XFxcMHv2bCQkJGDSpEk0YBGtunr1Knbt2oXdu3cjNzcXbm5umDt3LhYuXIjQ0FC+y9M5yhvRJ3PPG7knagrJgxUVFWHfvn3YtWsX0tLSYGtri/Hjx2PKlCmIi4vDkCFD+C6RGJnGxkZkZGQgOTkZBw4cwJUrV9CvXz9MmzYNCQkJmDZtGkQiEd9l8oLyRrSN8kYeEjWF5NEUFhbi8OHDOHbsGE6cOIHGxkb4+/tj6tSpmDJlCsLCwuDm5sZ3mcTAKJVKZGZm4tdff8WxY8dw9uxZAMCYMWMwdepUxMbGIjQ0FBYWdEKEO1HeSE9Q3kgPUVNIeq6trQ1paWn4+eefcezYMZw/fx6dnZ3w8/NDWFgYQkNDERYWhuHDh9OnUDMjk8mQmpqKjIwMpKWlIScnByqVCj4+Ppg6dSqio6MRFRUFJycnvks1GpQ3ci+UN6Il1BQS7VEoFMjIyEB6ejrS09ORkZGB+vp62NnZYfTo0QgODkZISAiCg4MxdOhQWFlZ8V0y6SXGGG7cuIHs7Gzk5OQgOzsbWVlZKCsrg1gsRkhICEJDQxEaGorw8HC6PqoWUd7MD+WN6Bg1hUR3Ojs7ceXKFaSnpyMzMxPZ2dm4dOkSmpqaIBaLERQUhODgYIwYMQKDBw9GYGAg/Pz8aCuHgSorK0N+fj4KCgpw8eJF5OTk4OLFi2hoaIBQKMSgQYMQHByMUaNGITQ0FGPGjIFEIuG7bLNBeTMtlDfCA2oKiX51dHTg2rVr3KfcnJwc5ObmorS0FAAgFovh5+eHoKAgBAQEcDdfX194enrSAKZjlZWVKC4uxvXr15Gfn4/8/Hxcu3YNBQUFaGhoAABIpVIMHTqU2xIVEhKCYcOGwcbGhufqyd0ob4aN8kYMDDWFxDA0NDSgoKAA165d41aOBQUFGitHoVAIDw8P+Pj4cDdvb294e3ujf//+cHFxgbOzM+08fQ8KhQLl5eWoqKiATCaDTCZDcXExiouLud9bWloA/NYsDBgwAIGBgRrNQlBQEFxdXXl+JaS3KG+6R3kjRoiaQmL4KioqUFRU1GWlWlRUhOLiYtTX13PzCoVCODs7w8XFBe7u7nBxcYGLiws8PDzg5OQEBwcHODg4wNHRkbsvlUp5fHU909zcDLlcjrq6OtTV1XH3a2trUV1djfLyclRWVqK6uhplZWWoqqriBiAAsLKy4gZ4b29v+Pj4wNfXl7vv7e1NW4nMFOWtK8obMRPUFBLjV19fj1u3bmmskNX3q6urUVlZifLycsjlciiVyi5/b2Fh8f/buZsVB2EoDMOfYBSsrbrw9/53cyNzD3MRajFCDIUuijLW3Ww6M32flZxAIsLxnEhwV7ziOFaSJEqSRHEc63K5KAxD5XkuY8z2l/8gCJTn+WG+8/l8eMEPw6DnVLPWalkWSZJzTvM8b7FxHHW73TQMg7z3mqZJ1tqtGDnnDutGUaSiKFRVleq6VtM0KstSbduqrmuVZamu61RVlZqmURAEP37meF/k2wP5hn+IphDvxXu/2/F/3/Wv18uyyFqreZ7lnNP1epX3XuM4bmPrXNM0Hdbo+/4QS9NUxphdbC2G0qPAnE6nXWE0xijLsm0sTdOtmD5/fSmKgjNG+HXIN+BPoSkEAACAPjkhDAAAANEUAgAAgKYQAAAAUijp49U3AQAAgJf6ugP/PMncU86duQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -259,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -302,12 +302,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -338,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -347,17 +347,17 @@
      "text": [
       "Output of build task graph are instances of each task in a dictionary:\n",
       "\n",
-      "load_csv_data: <gquant.plugin_nodes.dataloader.csvStockLoader.CsvStockLoader object at 0x7f559c390320>\n",
-      "min_volume: <gquant.plugin_nodes.transform.volumeFilterNode.VolumeFilterNode object at 0x7f559c390400>\n",
-      "sort: <gquant.plugin_nodes.transform.sortNode.SortNode object at 0x7f559c390438>\n",
-      "add_return: <gquant.plugin_nodes.transform.returnFeatureNode.ReturnFeatureNode object at 0x7f557f6675c0>\n",
-      "stock_symbol: <gquant.plugin_nodes.dataloader.stockNameLoader.StockNameLoader object at 0x7f557f667cc0>\n",
-      "volume_mean: <gquant.plugin_nodes.transform.averageNode.AverageNode object at 0x7f557f667dd8>\n",
-      "return_mean: <gquant.plugin_nodes.transform.averageNode.AverageNode object at 0x7f557f667e48>\n",
-      "left_merge_1: <gquant.plugin_nodes.transform.leftMergeNode.LeftMergeNode object at 0x7f557f667eb8>\n",
-      "left_merge_2: <gquant.plugin_nodes.transform.leftMergeNode.LeftMergeNode object at 0x7f557f667ef0>\n",
-      "output_csv_1: <gquant.plugin_nodes.analysis.outCsvNode.OutCsvNode object at 0x7f557f667f60>\n",
-      "output_csv_2: <gquant.plugin_nodes.analysis.outCsvNode.OutCsvNode object at 0x7f557f667fd0>\n",
+      "load_csv_data: <NodeInTaskGraph gquant.plugin_nodes.dataloader.csvStockLoader.CsvStockLoader object at 0x7f2581c22358>\n",
+      "min_volume: <NodeInTaskGraph gquant.plugin_nodes.transform.volumeFilterNode.VolumeFilterNode object at 0x7f2581a1a240>\n",
+      "sort: <NodeInTaskGraph gquant.plugin_nodes.transform.sortNode.SortNode object at 0x7f25809908d0>\n",
+      "add_return: <NodeInTaskGraph gquant.plugin_nodes.transform.returnFeatureNode.ReturnFeatureNode object at 0x7f2580990ac8>\n",
+      "stock_symbol: <NodeInTaskGraph gquant.plugin_nodes.dataloader.stockNameLoader.StockNameLoader object at 0x7f2580990cc0>\n",
+      "volume_mean: <NodeInTaskGraph gquant.plugin_nodes.transform.averageNode.AverageNode object at 0x7f2580990eb8>\n",
+      "return_mean: <NodeInTaskGraph gquant.plugin_nodes.transform.averageNode.AverageNode object at 0x7f25809a40f0>\n",
+      "left_merge_1: <NodeInTaskGraph gquant.plugin_nodes.transform.leftMergeNode.LeftMergeNode object at 0x7f25809a42e8>\n",
+      "left_merge_2: <NodeInTaskGraph gquant.plugin_nodes.transform.leftMergeNode.LeftMergeNode object at 0x7f25809a44e0>\n",
+      "output_csv_1: <NodeInTaskGraph gquant.plugin_nodes.analysis.outCsvNode.OutCsvNode object at 0x7f25809a46d8>\n",
+      "output_csv_2: <NodeInTaskGraph gquant.plugin_nodes.analysis.outCsvNode.OutCsvNode object at 0x7f25809a48d0>\n",
       "\n"
      ]
     }
@@ -373,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -382,10 +382,8 @@
      "text": [
       "Input columns in incoming dataframes:\n",
       "\n",
-      "{<gquant.plugin_nodes.dataloader.stockNameLoader.StockNameLoader object at 0x7f557f667cc0>: {'asset': 'int64',\n",
-      "                                                                                             'asset_name': 'object'},\n",
-      " <gquant.plugin_nodes.transform.averageNode.AverageNode object at 0x7f557f667dd8>: {'asset': 'int64',\n",
-      "                                                                                    'volume': 'float64'}}\n"
+      "{0: {'asset': 'int64', 'volume': 'float64'},\n",
+      " 1: {'asset': 'int64', 'asset_name': 'object'}}\n"
      ]
     }
    ],
@@ -398,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -443,7 +441,42 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's inspect the content of `csv_1_df` and `csv_2_df`."
+    "We can profile each of the computation node running time by turning on the profiler."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id:load_csv_data process time:56.329\n",
+      "id:min_volume process time:0.134\n",
+      "id:sort process time:0.155\n",
+      "id:add_return process time:0.186\n",
+      "id:volume_mean process time:0.025\n",
+      "id:return_mean process time:0.027\n",
+      "id:stock_symbol process time:0.013\n",
+      "id:left_merge_1 process time:0.008\n",
+      "id:output_csv_1 process time:0.013\n",
+      "id:left_merge_2 process time:0.005\n",
+      "id:output_csv_2 process time:0.013\n"
+     ]
+    }
+   ],
+   "source": [
+    "outputs = ['load_csv_data', 'output_csv_1', 'output_csv_2']\n",
+    "csv_data_df, csv_1_df, csv_2_df = task_graph.run(outputs=outputs, profile=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Where most of the time is spent on the csv file processing. This is because we have to convert the time string to the proper format via CPU. Let's inspect the content of `csv_1_df` and `csv_2_df`."
    ]
   },
   {
@@ -461,129 +494,129 @@
       "1     869589   110.456066       DSLV\n",
       "2     869590    66.607253       BPTH\n",
       "3     869592    56.041766         SP\n",
-      "4     869349    91.161991       VIIX\n",
-      "5     869357   307.764991       USLV\n",
-      "6     869358   487.509967        UVE\n",
-      "7     869363   149.038448       SNOW\n",
-      "8     869368   130.891743       AMBR\n",
-      "9     869369   149.523665        IBP\n",
-      "10    869374   252.592963       WATT\n",
-      "11    869378    79.322242        VZA\n",
-      "12    869388  7665.878932        AAL\n",
-      "13    869391   625.306024       NADL\n",
-      "14    869392    60.814437       VUSE\n",
-      "15    869393   327.066727       AQXP\n",
-      "16    869397   142.715230        LCI\n",
-      "17    869398   423.170980       TWOU\n",
-      "18    869402   204.178634       CNCE\n",
-      "19    869404   863.266938       ATHM\n",
-      "20    869408   280.415556       ATNM\n",
-      "21    869409   382.002194         LE\n",
-      "22    869410   151.372894       VSAR\n",
-      "23    869415    96.677355       AKAO\n",
-      "24    869424   300.030156       CBPX\n",
-      "25    869425   178.626874       QTWO\n",
-      "26    869429   351.563573       TLMR\n",
-      "27    869434   130.247559       SZMK\n",
-      "28    869436    96.189895       ARGS\n",
-      "29    869438    55.945668       REPH\n",
+      "4      22252   504.761396        CEF\n",
+      "5      22254    66.178077       SKYY\n",
+      "6      22260   401.527545       CLDX\n",
+      "7      22262   536.560685       UNIS\n",
+      "8      22266  1395.477945        PLD\n",
+      "9      22281  2942.558898       SQQQ\n",
+      "10     22283   824.567980        HCN\n",
+      "11     22284    92.215897        CIK\n",
+      "12     22293   328.522166        FSP\n",
+      "13     22294   518.653193       ECYT\n",
+      "14     22303    73.952609       SUMR\n",
+      "15     22304   323.398782        PMC\n",
+      "16     22306  1188.195338       MTOR\n",
+      "17     22312   328.723606         RP\n",
+      "18     22316   845.681667       MTDR\n",
+      "19     22323  2589.050216        CIE\n",
+      "20     22338   918.747811       ROVI\n",
+      "21     22339   734.596191         NM\n",
+      "22     22348   601.541333       SYRG\n",
+      "23     22352   261.406284       SYMX\n",
+      "24     22355   393.671418       ACRX\n",
+      "25     22356  4343.659885         GG\n",
+      "26     22361   102.828032        BTX\n",
+      "27     22363  2082.957392        MSI\n",
+      "28     22364  2030.574212       SWFT\n",
+      "29     22370   260.718094       ARWR\n",
       "...      ...          ...        ...\n",
-      "3654    6044   798.955229        ERJ\n",
-      "3655    6046   109.921556        ESD\n",
-      "3656    6047   128.321656        ESE\n",
-      "3657    6048   513.030201        ESI\n",
-      "3658    6049   137.107665        ESL\n",
-      "3659    6050   243.799521        ESS\n",
-      "3660    6051  2159.787058        ESV\n",
-      "3661    6052    83.121500        ETB\n",
-      "3662    6053  2197.245195        ETE\n",
-      "3663    6054   200.542336        ETG\n",
-      "3664    6055   316.128865        ETH\n",
-      "3665    6056   260.454595        ETJ\n",
-      "3666    6057   248.049368        ETM\n",
-      "3667    6058  2041.957807        ETN\n",
-      "3668    6059    51.739112        ETO\n",
-      "3669    6060   726.054349        ETP\n",
-      "3670    6061  1020.273024        ETR\n",
-      "3671    6063   191.081808        ETV\n",
-      "3672    6064   332.996479        ETW\n",
-      "3673    6065   468.362683        ETY\n",
-      "3674    6066   601.402261         EV\n",
-      "3675    6067   345.731763        EVC\n",
-      "3676    6068   107.581641        EVF\n",
-      "3677    6069    61.711877        EVG\n",
-      "3678    6071   263.450712        EVR\n",
-      "3679    6072   177.854760        EVT\n",
-      "3680    6073  1013.643404         EW\n",
-      "3681    6089  3150.937855        EXC\n",
-      "3682    6090   967.046349        EXG\n",
-      "3683    6093   437.141584        EXP\n",
+      "3654   24022    80.863700       FULL\n",
+      "3655   24029   206.696661         JE\n",
+      "3656   24030   123.923580       TOWR\n",
+      "3657   24031   160.319024        HHC\n",
+      "3658   24032   125.691977        DPG\n",
+      "3659   24036   480.417818       JMBA\n",
+      "3660   24038   159.318593       HTHT\n",
+      "3661   24040   163.879043        TST\n",
+      "3662   24041    95.472417        PTN\n",
+      "3663   24046   128.900185        NTN\n",
+      "3664   24051   592.212903       POST\n",
+      "3665   24053    78.732765       AOSL\n",
+      "3666   24059    54.465283        HEQ\n",
+      "3667   24065  1215.237406       SMFG\n",
+      "3668   24066   622.342256       MEMP\n",
+      "3669   24067   252.632282        AXU\n",
+      "3670   24069  1087.383937        DLR\n",
+      "3671   24072   233.026579        BBN\n",
+      "3672   24074   549.153817        BKU\n",
+      "3673   24076   295.160203        STV\n",
+      "3674   24077  1996.519771       INVN\n",
+      "3675   24078   147.643035        TCO\n",
+      "3676   24088    86.557899        MCF\n",
+      "3677   24100   146.701113       XOXO\n",
+      "3678   24108   177.371443       PRIM\n",
+      "3679   24112   626.713313       CLNY\n",
+      "3680   24114   444.717606        NSU\n",
+      "3681   24118  5990.215130         GS\n",
+      "3682   24121   221.881592        SMA\n",
+      "3683   24122   737.811219       ULTA\n",
       "\n",
       "[3684 rows x 3 columns]\n",
       "\n",
       "csv_2_df content:\n",
-      "       asset      returns asset_name\n",
-      "0     869584     0.000369        LPT\n",
-      "1     869589     0.001077       DSLV\n",
-      "2     869590     0.005321       BPTH\n",
-      "3     869592     0.000502         SP\n",
-      "4     869349     0.004717       VIIX\n",
-      "5     869357     0.005730       USLV\n",
-      "6     869358     0.001329        UVE\n",
-      "7     869363    -0.000029       SNOW\n",
-      "8     869368    -0.001582       AMBR\n",
-      "9     869369     0.001741        IBP\n",
-      "10    869374     0.000462       WATT\n",
-      "11    869378     0.000190        VZA\n",
-      "12    869388     0.001218        AAL\n",
-      "13    869391     0.013026       NADL\n",
-      "14    869392     0.000061       VUSE\n",
-      "15    869393     0.006601       AQXP\n",
-      "16    869397     0.206163        LCI\n",
-      "17    869398     0.001777       TWOU\n",
-      "18    869402     0.000691       CNCE\n",
-      "19    869404     0.000391       ATHM\n",
-      "20    869408     0.002258       ATNM\n",
-      "21    869409    -0.000727         LE\n",
-      "22    869410    -0.001565       VSAR\n",
-      "23    869415    -0.002377       AKAO\n",
-      "24    869424     0.000978       CBPX\n",
-      "25    869425     0.001198       QTWO\n",
-      "26    869429     0.000663       TLMR\n",
-      "27    869434    -0.002273       SZMK\n",
-      "28    869436     0.001104       ARGS\n",
-      "29    869438     0.000922       REPH\n",
-      "...      ...          ...        ...\n",
-      "3654    5801    -0.000032        DFP\n",
-      "3655    5806     0.000907         DG\n",
-      "3656    5809     0.000807        DGX\n",
-      "3657    5810    -0.000196        DHF\n",
-      "3658    5811     0.000044        DHG\n",
-      "3659    5812     0.000695        DHI\n",
-      "3660    5814     0.000539        DHR\n",
-      "3661    5816     0.003578        DHT\n",
-      "3662    5817     0.000315        DHX\n",
-      "3663    5819     0.000312        DIS\n",
-      "3664    5825     0.000486         DK\n",
-      "3665    5831     0.000453        DKL\n",
-      "3666    5836     0.000712        DKS\n",
-      "3667    5837     0.000213        DKT\n",
-      "3668    5841     0.000496        DLB\n",
-      "3669    5849     0.000393        DLX\n",
-      "3670    5854     0.000513        DNB\n",
-      "3671    5858     0.000092        DNP\n",
-      "3672    5859     0.000848        DNR\n",
-      "3673    5860     0.000413         DO\n",
-      "3674    5865     0.000303        DOV\n",
-      "3675    5866     0.000228        DOW\n",
-      "3676    5871     0.000443        DPM\n",
-      "3677    5882     0.001049        DRE\n",
-      "3678    5889     0.000432        DRH\n",
-      "3679    5890     0.000614        DRI\n",
-      "3680    5891  1899.939370        DRL\n",
-      "3681    5893     0.000607        DRQ\n",
-      "3682    5896    -0.000400        DSL\n",
-      "3683    5897     0.000033        DSM\n",
+      "       asset   returns asset_name\n",
+      "0     869584  0.000369        LPT\n",
+      "1     869589  0.001077       DSLV\n",
+      "2     869590  0.005321       BPTH\n",
+      "3     869592  0.000502         SP\n",
+      "4     708893 -0.000588        UCP\n",
+      "5     708921  0.000156       USAC\n",
+      "6     708931  0.000799       USPH\n",
+      "7     708953 -0.000139       VEEV\n",
+      "8     708964 -0.001573       VJET\n",
+      "9     708967  0.000819       VLRS\n",
+      "10    708973 -0.003376       VMEM\n",
+      "11    708986  0.000715       VOYA\n",
+      "12    709003  0.009236        WAC\n",
+      "13    709005  0.001957       WAGE\n",
+      "14    709016  0.000328       WCIC\n",
+      "15    709019  0.000788       WDAY\n",
+      "16    709024  0.000875        WEX\n",
+      "17    709038  0.000875        WGP\n",
+      "18    709047 -0.000371        WLH\n",
+      "19    709054 -0.000588        WMC\n",
+      "20    709061  0.000213       WNRL\n",
+      "21    709082  0.000187        WSR\n",
+      "22    709090  0.001853       WUBA\n",
+      "23    709091  0.001273       WWAV\n",
+      "24    709112  0.001282        XON\n",
+      "25    709114  0.002133        XPO\n",
+      "26    709127 -0.000696       YUME\n",
+      "27    709143  0.000624        ZTS\n",
+      "28    767696  0.001142       CANF\n",
+      "29    795074  0.000044       NVGS\n",
+      "...      ...       ...        ...\n",
+      "3654   22418  0.000232       NWSA\n",
+      "3655   22431  0.003480        NCT\n",
+      "3656   22433  0.000768         MD\n",
+      "3657   22436  0.000234        NLY\n",
+      "3658   22437  0.000011       VGSH\n",
+      "3659   22440  0.000463        EQR\n",
+      "3660   22444  0.000490       DNKN\n",
+      "3661   22449  0.001112        LYB\n",
+      "3662   22450  0.001024         KS\n",
+      "3663   22451  0.000637       LSTR\n",
+      "3664   22456 -0.000242       RBCN\n",
+      "3665   22457 -0.000096        CVE\n",
+      "3666   22459  0.000232        CUR\n",
+      "3667   22460  0.001203        VAC\n",
+      "3668   22461 -0.000239         MY\n",
+      "3669   22463  0.000429        NAK\n",
+      "3670   22465  0.001771        NAV\n",
+      "3671   22474  0.000002       ACWX\n",
+      "3672   22476  0.000342        PEI\n",
+      "3673   22477  0.000309         HI\n",
+      "3674   22481  0.001255        SRV\n",
+      "3675   22486  0.000533        THR\n",
+      "3676   22492  0.000203        RLJ\n",
+      "3677   22494  0.000740       BRFS\n",
+      "3678   22499  0.002204        LNG\n",
+      "3679   22500  0.000173        ANH\n",
+      "3680   22503  0.022120       VRML\n",
+      "3681   22505  0.000514        GNE\n",
+      "3682   22507  0.000102         ZN\n",
+      "3683   22508  0.001179       AXAS\n",
       "\n",
       "[3684 rows x 3 columns]\n"
      ]
diff --git a/notebooks/02_single_stock_trade.ipynb b/notebooks/02_single_stock_trade.ipynb
index 6284b316..ae6355d8 100644
--- a/notebooks/02_single_stock_trade.ipynb
+++ b/notebooks/02_single_stock_trade.ipynb
@@ -14,8 +14,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
-    "sys.path.append('..')\n",
+    "import sys; sys.path.insert(0, '..')\n",
     "import os\n",
     "import warnings\n",
     "import ipywidgets as widgets\n",
diff --git a/notebooks/03_simple_dask_example.ipynb b/notebooks/03_simple_dask_example.ipynb
index c3076c1e..674e0f87 100644
--- a/notebooks/03_simple_dask_example.ipynb
+++ b/notebooks/03_simple_dask_example.ipynb
@@ -6,8 +6,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
-    "sys.path.append('..')\n",
+    "import sys; sys.path.insert(0, '..')\n",
     "\n",
     "from gquant.dataframe_flow import TaskGraph"
    ]
@@ -25,23 +24,23 @@
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3 style=\"text-align: left;\">Client</h3>\n",
        "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
-       "  <li><b>Scheduler: </b>tcp://127.0.0.1:46190</li>\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:36927</li>\n",
        "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
        "</ul>\n",
        "</td>\n",
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3 style=\"text-align: left;\">Cluster</h3>\n",
        "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
-       "  <li><b>Workers: </b>8</li>\n",
-       "  <li><b>Cores: </b>8</li>\n",
-       "  <li><b>Memory: </b>540.95 GB</li>\n",
+       "  <li><b>Workers: </b>4</li>\n",
+       "  <li><b>Cores: </b>4</li>\n",
+       "  <li><b>Memory: </b>270.39 GB</li>\n",
        "</ul>\n",
        "</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "text/plain": [
-       "<Client: 'tcp://127.0.0.1:46190' processes=8 threads=8, memory=540.95 GB>"
+       "<Client: 'tcp://127.0.0.1:36927' processes=4 threads=4, memory=270.39 GB>"
       ]
      },
      "execution_count": 2,
@@ -217,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -264,12 +263,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -285,11 +284,439 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id:node_csvdata_dask process time:0.738\n",
+      "id:node_minVolume process time:0.668\n",
+      "id:node_volumeMean process time:0.124\n",
+      "id:node_outputCsv process time:1.708\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = task_graph.run(['node_outputCsv'], {}, profile=True)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>asset</th>\n",
+       "      <th>volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>631</td>\n",
+       "      <td>350.187622</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>914</td>\n",
+       "      <td>267.823241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1404</td>\n",
+       "      <td>2073.026646</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1544</td>\n",
+       "      <td>80.645555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1545</td>\n",
+       "      <td>18920.967128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1551</td>\n",
+       "      <td>137.250647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1556</td>\n",
+       "      <td>255.882891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1562</td>\n",
+       "      <td>185.334286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1565</td>\n",
+       "      <td>66.781550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1568</td>\n",
+       "      <td>948.372821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1570</td>\n",
+       "      <td>2026.146426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1571</td>\n",
+       "      <td>105.151335</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1576</td>\n",
+       "      <td>97.030780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1578</td>\n",
+       "      <td>544.907474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1580</td>\n",
+       "      <td>1387.531068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1581</td>\n",
+       "      <td>311.916046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1583</td>\n",
+       "      <td>984.979860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1586</td>\n",
+       "      <td>624.883842</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1587</td>\n",
+       "      <td>66.372422</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1589</td>\n",
+       "      <td>335.873462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1592</td>\n",
+       "      <td>127.409332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1595</td>\n",
+       "      <td>523.506921</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1597</td>\n",
+       "      <td>69.508729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1598</td>\n",
+       "      <td>5667.614178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1609</td>\n",
+       "      <td>128.506439</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1611</td>\n",
+       "      <td>2491.793377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1614</td>\n",
+       "      <td>890.201513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1619</td>\n",
+       "      <td>390.839224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1625</td>\n",
+       "      <td>267.357030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>1626</td>\n",
+       "      <td>52.506585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3654</th>\n",
+       "      <td>869489</td>\n",
+       "      <td>102.546743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3655</th>\n",
+       "      <td>869492</td>\n",
+       "      <td>140.843590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3656</th>\n",
+       "      <td>869497</td>\n",
+       "      <td>57.606204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3657</th>\n",
+       "      <td>869499</td>\n",
+       "      <td>268.340461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3658</th>\n",
+       "      <td>869502</td>\n",
+       "      <td>53.870074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3659</th>\n",
+       "      <td>869504</td>\n",
+       "      <td>74.247626</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3660</th>\n",
+       "      <td>869509</td>\n",
+       "      <td>1699.036940</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3661</th>\n",
+       "      <td>869510</td>\n",
+       "      <td>432.329984</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3662</th>\n",
+       "      <td>869511</td>\n",
+       "      <td>224.161922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3663</th>\n",
+       "      <td>869517</td>\n",
+       "      <td>181.235935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3664</th>\n",
+       "      <td>869527</td>\n",
+       "      <td>54.682459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3665</th>\n",
+       "      <td>869532</td>\n",
+       "      <td>123.373937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3666</th>\n",
+       "      <td>869533</td>\n",
+       "      <td>8778.535845</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3667</th>\n",
+       "      <td>869535</td>\n",
+       "      <td>191.725729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3668</th>\n",
+       "      <td>869539</td>\n",
+       "      <td>127.848087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3669</th>\n",
+       "      <td>869541</td>\n",
+       "      <td>2002.128376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3670</th>\n",
+       "      <td>869543</td>\n",
+       "      <td>348.918650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3671</th>\n",
+       "      <td>869544</td>\n",
+       "      <td>137.447304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3672</th>\n",
+       "      <td>869546</td>\n",
+       "      <td>223.552076</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3673</th>\n",
+       "      <td>869551</td>\n",
+       "      <td>225.113978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3674</th>\n",
+       "      <td>869554</td>\n",
+       "      <td>867.364437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3675</th>\n",
+       "      <td>869557</td>\n",
+       "      <td>110.941843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3676</th>\n",
+       "      <td>869558</td>\n",
+       "      <td>1120.226836</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3677</th>\n",
+       "      <td>869567</td>\n",
+       "      <td>239.959380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3678</th>\n",
+       "      <td>869571</td>\n",
+       "      <td>658.857428</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3679</th>\n",
+       "      <td>869577</td>\n",
+       "      <td>150.850651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3680</th>\n",
+       "      <td>869584</td>\n",
+       "      <td>673.502241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3681</th>\n",
+       "      <td>869589</td>\n",
+       "      <td>110.377576</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3682</th>\n",
+       "      <td>869590</td>\n",
+       "      <td>66.575254</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3683</th>\n",
+       "      <td>869592</td>\n",
+       "      <td>56.085032</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3684 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       asset        volume\n",
+       "0        631    350.187622\n",
+       "1        914    267.823241\n",
+       "2       1404   2073.026646\n",
+       "3       1544     80.645555\n",
+       "4       1545  18920.967128\n",
+       "5       1551    137.250647\n",
+       "6       1556    255.882891\n",
+       "7       1562    185.334286\n",
+       "8       1565     66.781550\n",
+       "9       1568    948.372821\n",
+       "10      1570   2026.146426\n",
+       "11      1571    105.151335\n",
+       "12      1576     97.030780\n",
+       "13      1578    544.907474\n",
+       "14      1580   1387.531068\n",
+       "15      1581    311.916046\n",
+       "16      1583    984.979860\n",
+       "17      1586    624.883842\n",
+       "18      1587     66.372422\n",
+       "19      1589    335.873462\n",
+       "20      1592    127.409332\n",
+       "21      1595    523.506921\n",
+       "22      1597     69.508729\n",
+       "23      1598   5667.614178\n",
+       "24      1609    128.506439\n",
+       "25      1611   2491.793377\n",
+       "26      1614    890.201513\n",
+       "27      1619    390.839224\n",
+       "28      1625    267.357030\n",
+       "29      1626     52.506585\n",
+       "...      ...           ...\n",
+       "3654  869489    102.546743\n",
+       "3655  869492    140.843590\n",
+       "3656  869497     57.606204\n",
+       "3657  869499    268.340461\n",
+       "3658  869502     53.870074\n",
+       "3659  869504     74.247626\n",
+       "3660  869509   1699.036940\n",
+       "3661  869510    432.329984\n",
+       "3662  869511    224.161922\n",
+       "3663  869517    181.235935\n",
+       "3664  869527     54.682459\n",
+       "3665  869532    123.373937\n",
+       "3666  869533   8778.535845\n",
+       "3667  869535    191.725729\n",
+       "3668  869539    127.848087\n",
+       "3669  869541   2002.128376\n",
+       "3670  869543    348.918650\n",
+       "3671  869544    137.447304\n",
+       "3672  869546    223.552076\n",
+       "3673  869551    225.113978\n",
+       "3674  869554    867.364437\n",
+       "3675  869557    110.941843\n",
+       "3676  869558   1120.226836\n",
+       "3677  869567    239.959380\n",
+       "3678  869571    658.857428\n",
+       "3679  869577    150.850651\n",
+       "3680  869584    673.502241\n",
+       "3681  869589    110.377576\n",
+       "3682  869590     66.575254\n",
+       "3683  869592     56.085032\n",
+       "\n",
+       "[3684 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "df = task_graph.run(['node_outputCsv'], {})[0]"
+    "df"
    ]
   },
   {
diff --git a/notebooks/04_portfolio_trade.ipynb b/notebooks/04_portfolio_trade.ipynb
index c0327bf2..b9bee048 100644
--- a/notebooks/04_portfolio_trade.ipynb
+++ b/notebooks/04_portfolio_trade.ipynb
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -133,12 +133,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -148,7 +148,7 @@
     }
    ],
    "source": [
-    "import sys ; sys.path.append('..')\n",
+    "import sys; sys.path.insert(0, '..')\n",
     "from gquant.dataframe_flow import TaskGraph\n",
     "\n",
     "task_graph = TaskGraph.load_taskgraph('../task_example/port_trade.yaml')\n",
@@ -265,8 +265,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 4.89 s, sys: 3.11 s, total: 8 s\n",
-      "Wall time: 14.7 s\n"
+      "id:sort process time:0.268\n",
+      "id:add_return process time:1.427\n",
+      "id:add_indicator process time:0.238\n",
+      "id:volume_mean process time:0.062\n",
+      "id:rename_mean_volume process time:0.001\n",
+      "id:left_merge_mean_volume process time:2.680\n",
+      "id:max_returns process time:0.026\n",
+      "id:rename_max_return process time:0.001\n",
+      "id:left_merge_max_return process time:0.039\n",
+      "id:min_returns process time:0.027\n",
+      "id:rename_min_return process time:0.001\n",
+      "id:left_merge_min_return process time:0.055\n",
+      "id:filter_value process time:0.165\n",
+      "id:drop_columns process time:0.036\n",
+      "id:sort_2 process time:0.072\n",
+      "id:exp_strategy process time:0.780\n",
+      "id:backtest process time:0.002\n",
+      "id:portfolio_opt process time:0.020\n",
+      "id:sharpe_ratio process time:0.001\n",
+      "id:cumlative_return process time:0.611\n",
+      "CPU times: user 6.14 s, sys: 2.35 s, total: 8.49 s\n",
+      "Wall time: 8.65 s\n"
      ]
     }
    ],
@@ -278,7 +298,7 @@
     "            replace={'filter_value': {\"conf\": [{\"column\": \"volume_mean\", \"min\": min_volume},\n",
     "                                                   {\"column\": \"returns_max\", \"max\": max_rate},\n",
     "                                                   {\"column\": \"returns_min\", \"min\": min_rate}]},\n",
-    "                     'load_csv_data': {action: True}})\n",
+    "                     'load_csv_data': {action: True}}, profile=True)\n",
     "\n",
     "gpu_input_cached = o_gpu[2]  # 'load_csv_data' node output\n",
     "gpu_strategy_cached = o_gpu[3]  # 'sort_2' node output"
@@ -331,12 +351,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9988d64d19df4bfb820df823d42a5a43",
+       "model_id": "85c8cfb7fbe34ff89e057a665ace4384",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale()), Axis(label='Time', …"
+       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale(), side='left'), Axis(l…"
       ]
      },
      "metadata": {},
@@ -382,8 +402,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 2min 9s, sys: 14.8 s, total: 2min 24s\n",
-      "Wall time: 2min 24s\n"
+      "id:load_csv_data process time:59.484\n",
+      "id:sort process time:5.271\n",
+      "id:add_return process time:18.387\n",
+      "id:add_indicator process time:5.701\n",
+      "id:volume_mean process time:0.279\n",
+      "id:rename_mean_volume process time:0.001\n",
+      "id:left_merge_mean_volume process time:3.087\n",
+      "id:max_returns process time:0.277\n",
+      "id:rename_max_return process time:0.001\n",
+      "id:left_merge_max_return process time:2.836\n",
+      "id:min_returns process time:0.278\n",
+      "id:rename_min_return process time:0.001\n",
+      "id:left_merge_min_return process time:2.942\n",
+      "id:filter_value process time:0.931\n",
+      "id:drop_columns process time:0.059\n",
+      "id:sort_2 process time:1.032\n",
+      "id:exp_strategy process time:9.772\n",
+      "id:backtest process time:0.103\n",
+      "id:portfolio_opt process time:0.286\n",
+      "id:sharpe_ratio process time:0.001\n",
+      "id:cumlative_return process time:0.057\n",
+      "CPU times: user 1min 44s, sys: 6.73 s, total: 1min 51s\n",
+      "Wall time: 1min 50s\n"
      ]
     }
    ],
@@ -397,7 +438,7 @@
     "                                               {\"column\": \"returns_min\", \"min\": min_rate}]},\n",
     "                     'add_return': {\"type\": \"CpuReturnFeatureNode\"},\n",
     "                     'add_indicator': {\"type\": \"CpuAssetIndicatorNode\"},\n",
-    "                     'exp_strategy': {\"type\": \"CpuPortExpMovingAverageStrategyNode\"}})\n",
+    "                     'exp_strategy': {\"type\": \"CpuPortExpMovingAverageStrategyNode\"}}, profile=True)\n",
     "\n",
     "cpu_input_cached = o_cpu[2]  # 'load_csv_data' node output\n",
     "cpu_strategy_cached = o_cpu[3]  # 'sort_2' node output"
@@ -411,12 +452,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ac66026af35f450fac2c28b92a3f10eb",
+       "model_id": "aaae4209c51743bea12b452c48b0505d",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale()), Axis(label='Time', …"
+       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale(), side='left'), Axis(l…"
       ]
      },
      "metadata": {},
@@ -471,23 +512,23 @@
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3 style=\"text-align: left;\">Client</h3>\n",
        "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
-       "  <li><b>Scheduler: </b>tcp://127.0.0.1:35971</li>\n",
-       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:33545</li>\n",
+       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:34797/status' target='_blank'>http://127.0.0.1:34797/status</a>\n",
        "</ul>\n",
        "</td>\n",
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3 style=\"text-align: left;\">Cluster</h3>\n",
        "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
-       "  <li><b>Workers: </b>8</li>\n",
-       "  <li><b>Cores: </b>8</li>\n",
-       "  <li><b>Memory: </b>540.95 GB</li>\n",
+       "  <li><b>Workers: </b>4</li>\n",
+       "  <li><b>Cores: </b>4</li>\n",
+       "  <li><b>Memory: </b>270.39 GB</li>\n",
        "</ul>\n",
        "</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "text/plain": [
-       "<Client: 'tcp://127.0.0.1:35971' processes=8 threads=8, memory=540.95 GB>"
+       "<Client: 'tcp://127.0.0.1:33545' processes=4 threads=4, memory=270.39 GB>"
       ]
      },
      "execution_count": 11,
@@ -522,14 +563,14 @@
     {
      "data": {
       "text/plain": [
-       "['/Project/Projects/gQuant/notebooks/many-small/0.csv',\n",
-       " '/Project/Projects/gQuant/notebooks/many-small/1.csv',\n",
-       " '/Project/Projects/gQuant/notebooks/many-small/2.csv',\n",
-       " '/Project/Projects/gQuant/notebooks/many-small/3.csv',\n",
-       " '/Project/Projects/gQuant/notebooks/many-small/4.csv',\n",
-       " '/Project/Projects/gQuant/notebooks/many-small/5.csv',\n",
-       " '/Project/Projects/gQuant/notebooks/many-small/6.csv',\n",
-       " '/Project/Projects/gQuant/notebooks/many-small/7.csv']"
+       "['/Projects/gQuant/notebooks/many-small/0.csv',\n",
+       " '/Projects/gQuant/notebooks/many-small/1.csv',\n",
+       " '/Projects/gQuant/notebooks/many-small/2.csv',\n",
+       " '/Projects/gQuant/notebooks/many-small/3.csv',\n",
+       " '/Projects/gQuant/notebooks/many-small/4.csv',\n",
+       " '/Projects/gQuant/notebooks/many-small/5.csv',\n",
+       " '/Projects/gQuant/notebooks/many-small/6.csv',\n",
+       " '/Projects/gQuant/notebooks/many-small/7.csv']"
       ]
      },
      "execution_count": 12,
@@ -561,8 +602,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 21.3 s, sys: 4.3 s, total: 25.6 s\n",
-      "Wall time: 1min 25s\n"
+      "id:load_csv_data process time:0.072\n",
+      "id:volume_mean process time:0.149\n",
+      "id:rename_mean_volume process time:0.021\n",
+      "id:left_merge_mean_volume process time:0.334\n",
+      "id:max_returns process time:0.056\n",
+      "id:rename_max_return process time:0.021\n",
+      "id:left_merge_max_return process time:0.079\n",
+      "id:min_returns process time:0.068\n",
+      "id:rename_min_return process time:0.022\n",
+      "id:left_merge_min_return process time:0.080\n",
+      "id:filter_value process time:0.114\n",
+      "id:backtest process time:0.072\n",
+      "id:portfolio_opt process time:0.130\n",
+      "id:sharpe_ratio process time:10.098\n",
+      "id:cumlative_return process time:10.962\n",
+      "CPU times: user 48 s, sys: 1.75 s, total: 49.8 s\n",
+      "Wall time: 2min 25s\n"
      ]
     }
    ],
@@ -574,7 +630,7 @@
     "                                       \"conf\": {\"path\": \"many-small\"}},\n",
     "                     'filter_value': {\"conf\": [{\"column\": \"volume_mean\", \"min\": min_volume},\n",
     "                                               {\"column\": \"returns_max\", \"max\": max_rate},\n",
-    "                                               {\"column\": \"returns_min\", \"min\": min_rate}]}})\n",
+    "                                               {\"column\": \"returns_min\", \"min\": min_rate}]}}, profile=True)\n",
     "\n",
     "dask_input_cached = o_dask[2]  # 'load_csv_data' node output\n",
     "dask_strategy_cached = o_dask[3]  # 'sort_2' node output"
@@ -582,13 +638,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5fc144523eb6412c97369c56546693dd",
+       "model_id": "98089adc55194d2c965725f939079c64",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/notebooks/05_customize_nodes.ipynb b/notebooks/05_customize_nodes.ipynb
index 288057eb..b773d335 100644
--- a/notebooks/05_customize_nodes.ipynb
+++ b/notebooks/05_customize_nodes.ipynb
@@ -18,8 +18,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
-    "sys.path.append('..')\n",
+    "import sys; sys.path.insert(0, '..')\n",
     "\n",
     "# Load necessary Python modules\n",
     "import sys\n",
diff --git a/notebooks/05b_customize_nodes_with_ports.ipynb b/notebooks/05b_customize_nodes_with_ports.ipynb
index 8a5857a9..88521331 100644
--- a/notebooks/05b_customize_nodes_with_ports.ipynb
+++ b/notebooks/05b_customize_nodes_with_ports.ipynb
@@ -20,9 +20,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
-    "sys.path.append('..')\n",
-    "\n",
+    "import sys; sys.path.insert(0, '..')\n",
     "# Load necessary Python modules\n",
     "import sys\n",
     "from gquant.dataframe_flow import TaskSpecSchema, TaskGraph\n",
@@ -1564,7 +1562,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "py36-rapids0.10",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1578,7 +1576,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/06_xgboost_trade.ipynb b/notebooks/06_xgboost_trade.ipynb
index 903f026b..72a661e0 100644
--- a/notebooks/06_xgboost_trade.ipynb
+++ b/notebooks/06_xgboost_trade.ipynb
@@ -19,12 +19,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
-    "sys.path.append('..')\n",
+    "import sys; sys.path.insert(0, '..')\n",
     "\n",
     "import warnings\n",
     "from gquant.dataframe_flow import TaskGraph\n",
@@ -43,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,14 +60,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.10.0\n"
+      "0.11.0\n"
      ]
     }
    ],
@@ -105,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -157,12 +156,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -185,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -267,14 +266,13 @@
       "        train_cols = set(model_df.columns) - set(\n",
       "            self.conf['no_feature'].keys())\n",
       "        train_cols = list(train_cols - set([self.conf['target']]))\n",
-      "        pd_model = model_df.to_pandas()\n",
-      "        train = pd_model[train_cols]\n",
-      "        target = pd_model[self.conf['target']]\n",
-      "        dmatrix = xgb.DMatrix(train, target)\n",
+      "        train = model_df[train_cols]\n",
+      "        target = model_df[self.conf['target']]\n",
+      "        dmatrix = xgb.DMatrix(train, label=target)\n",
       "        bst = xgb.train(dxgb_params, dmatrix,\n",
       "                        num_boost_round=dxgb_params['nround'])\n",
       "        # make inferences\n",
-      "        infer_dmatrix = xgb.DMatrix(input_df.to_pandas()[train_cols])\n",
+      "        infer_dmatrix = xgb.DMatrix(input_df[train_cols])\n",
       "        prediction = cudf.Series(bst.predict(infer_dmatrix)).astype('float64')\n",
       "        signal = compute_signal(prediction)\n",
       "        input_df['signal'] = signal\n",
@@ -303,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -347,9 +345,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id:node_sort process time:0.271\n",
+      "id:node_addReturn process time:1.451\n",
+      "id:node_addIndicator process time:0.241\n",
+      "id:node_volumeMean process time:0.056\n",
+      "id:node_renameMeanVolume process time:0.001\n",
+      "id:node_leftMergeMeanVolume process time:2.728\n",
+      "id:node_maxReturns process time:0.021\n",
+      "id:node_renameMaxReturn process time:0.001\n",
+      "id:node_leftMergeMaxReturn process time:0.035\n",
+      "id:node_minReturns process time:0.027\n",
+      "id:node_renameMinReturn process time:0.001\n",
+      "id:node_leftMergeMinReturn process time:0.055\n",
+      "id:node_filterValue process time:0.184\n",
+      "id:node_dropColumns process time:0.036\n",
+      "id:node_sort2 process time:0.094\n",
+      "id:node_technical_indicator process time:2.799\n",
+      "id:node_xgboost_strategy process time:1.278\n",
+      "id:node_backtest process time:0.002\n",
+      "id:node_training_df process time:0.133\n",
+      "id:node_portOpt2 process time:0.018\n",
+      "id:node_sharpe_training process time:0.001\n",
+      "id:node_cumlativeReturn_training process time:0.625\n",
+      "id:node_testing_df process time:0.030\n",
+      "id:node_portOpt1 process time:0.019\n",
+      "id:node_sharpe_testing process time:0.001\n",
+      "id:node_cumlativeReturn_testing process time:0.493\n"
+     ]
+    }
+   ],
    "source": [
     "\n",
     "action = \"load\" if os.path.isfile('./.cache/node_csvdata.hdf5') else \"save\"\n",
@@ -360,7 +391,7 @@
     "              'node_csvdata': {action: True}}\n",
     "o_gpu = task_graph.run(\n",
     "            outputs=outlist + ['node_sort2'],\n",
-    "            replace=replace_spec)\n",
+    "            replace=replace_spec, profile=True)\n",
     "cached_sort = o_gpu[4]"
    ]
   },
@@ -373,13 +404,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3ea35da1b6684e4baa8f23b48daf356a",
+       "model_id": "adfa373f1c5b47e0b850ec66338fd34f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -421,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -554,13 +585,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id:node_technical_indicator process time:2.489\n",
+      "id:node_xgboost_strategy process time:3.885\n",
+      "id:node_backtest process time:0.002\n",
+      "id:node_training_df process time:0.054\n",
+      "id:node_portOpt2 process time:0.034\n",
+      "id:node_sharpe_training process time:0.001\n",
+      "id:node_cumlativeReturn_training process time:0.471\n",
+      "id:node_testing_df process time:0.029\n",
+      "id:node_portOpt1 process time:0.018\n",
+      "id:node_sharpe_testing process time:0.001\n",
+      "id:node_cumlativeReturn_testing process time:0.479\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9587710d3852467bac5932d35a4d4542",
+       "model_id": "bfdfde29021d49609b4c27bd5883210e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -577,7 +625,7 @@
     "replace_spec['node_sort2'] = {\"load\": cached_sort}\n",
     "o_gpu = task_graph.run(\n",
     "            outputs=outlist,\n",
-    "            replace=replace_spec)\n",
+    "            replace=replace_spec, profile=True)\n",
     "plot_figures(o_gpu)"
    ]
   },
@@ -600,13 +648,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5cd0350b70f6426c935e3313c688a554",
+       "model_id": "d45e1b5cf091436c9c29a0779c74d5bd",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/notebooks/07_fractional_differencing.ipynb b/notebooks/07_fractional_differencing.ipynb
index b62e55e5..14699ba2 100644
--- a/notebooks/07_fractional_differencing.ipynb
+++ b/notebooks/07_fractional_differencing.ipynb
@@ -21,8 +21,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
-    "sys.path.append('..')\n",
+    "import sys; sys.path.insert(0, '..')\n",
     "\n",
     "import warnings\n",
     "import gquant\n",
diff --git a/setup.py b/setup.py
index c8664d0f..1979587e 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 
 setup(
     name='gquant',
-    version='0.1',
+    version='0.3',
     description='gquant - RAPIDS Financial Services Algorithms',
     author='NVIDIA Corporation',
     packages=find_packages(include=['gquant', 'gquant.*']),

From 68acfe34ee1e6b43b63b97ec2a99afb0b20735c9 Mon Sep 17 00:00:00 2001
From: yidong72 <43824965+yidong72@users.noreply.github.com>
Date: Tue, 3 Mar 2020 15:42:49 -0500
Subject: [PATCH 4/6] [REVIEW] asian barrier option  tutorial (#77)

* initial checking the barrier option example

* added the CUDA way of doing option pricing

* used a more accurate model

* tensorrt only works with 512

* move the checkpoint data file to data.world

* to make the notebook run from beginning to the end in 16G v100

* fixed the memory reuse issue with cupy

* added comments how to replicate
---
 notebooks/asian_barrier_option/Makefile       |   11 +
 notebooks/asian_barrier_option/README.md      |   39 +
 .../asian_barrier_option/cuda_pricing.cu      |  190 +++
 .../deep_learning_nemo.ipynb                  |  550 +++++++++
 .../deep_learning_option_1.ipynb              |  883 +++++++++++++
 .../deep_learning_option_2.ipynb              | 1100 +++++++++++++++++
 .../asian_barrier_option/docker/Dockerfile    |   39 +
 .../asian_barrier_option/download_data.sh     |    6 +
 .../elu_activation/CMakeLists.txt             |   69 ++
 .../elu_activation/log/common.h               |  907 ++++++++++++++
 .../elu_activation/log/logger.cpp             |   35 +
 .../elu_activation/log/logger.h               |   31 +
 .../elu_activation/log/logging.h              |  503 ++++++++
 .../elu_activation/plugins/eluPlugin.cu       |  292 +++++
 .../elu_activation/plugins/eluPlugin.h        |  102 ++
 .../elu_activation/plugins/pluginKernels.h    |  237 ++++
 .../elu_activation/plugins/pluginUtil.h       |  375 ++++++
 notebooks/asian_barrier_option/helper_cuda.h  |  898 ++++++++++++++
 .../asian_barrier_option/helper_string.h      |  683 ++++++++++
 notebooks/asian_barrier_option/index.ipynb    |   79 ++
 .../asian_barrier_option/mc_pricing.ipynb     |  900 ++++++++++++++
 notebooks/asian_barrier_option/tensorrt.ipynb |  532 ++++++++
 22 files changed, 8461 insertions(+)
 create mode 100644 notebooks/asian_barrier_option/Makefile
 create mode 100644 notebooks/asian_barrier_option/README.md
 create mode 100644 notebooks/asian_barrier_option/cuda_pricing.cu
 create mode 100644 notebooks/asian_barrier_option/deep_learning_nemo.ipynb
 create mode 100644 notebooks/asian_barrier_option/deep_learning_option_1.ipynb
 create mode 100644 notebooks/asian_barrier_option/deep_learning_option_2.ipynb
 create mode 100644 notebooks/asian_barrier_option/docker/Dockerfile
 create mode 100755 notebooks/asian_barrier_option/download_data.sh
 create mode 100644 notebooks/asian_barrier_option/elu_activation/CMakeLists.txt
 create mode 100644 notebooks/asian_barrier_option/elu_activation/log/common.h
 create mode 100644 notebooks/asian_barrier_option/elu_activation/log/logger.cpp
 create mode 100644 notebooks/asian_barrier_option/elu_activation/log/logger.h
 create mode 100644 notebooks/asian_barrier_option/elu_activation/log/logging.h
 create mode 100644 notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.cu
 create mode 100644 notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h
 create mode 100644 notebooks/asian_barrier_option/elu_activation/plugins/pluginKernels.h
 create mode 100644 notebooks/asian_barrier_option/elu_activation/plugins/pluginUtil.h
 create mode 100644 notebooks/asian_barrier_option/helper_cuda.h
 create mode 100644 notebooks/asian_barrier_option/helper_string.h
 create mode 100644 notebooks/asian_barrier_option/index.ipynb
 create mode 100644 notebooks/asian_barrier_option/mc_pricing.ipynb
 create mode 100644 notebooks/asian_barrier_option/tensorrt.ipynb

diff --git a/notebooks/asian_barrier_option/Makefile b/notebooks/asian_barrier_option/Makefile
new file mode 100644
index 00000000..b6ed956e
--- /dev/null
+++ b/notebooks/asian_barrier_option/Makefile
@@ -0,0 +1,11 @@
+CUDA_HOME ?= /usr/local/cuda/
+NVCC ?= nvcc -O3 -DGPUTIMING # -lineinfo
+
+INCLUDES ?= -I$(CUDA_HOME)/include -I.
+
+LIBS ?= -L$(CUDA_HOME)/lib64 -lcudart -lcurand 
+
+NVFLAGS ?= -std=c++11 -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_70,code=sm_70 
+# Compile cuda source codes to objects
+out: cuda_pricing.cu
+	$(NVCC) $(NVFLAGS) $(INCLUDES) $(LIBS) -o $@ $<
diff --git a/notebooks/asian_barrier_option/README.md b/notebooks/asian_barrier_option/README.md
new file mode 100644
index 00000000..3d58e0c9
--- /dev/null
+++ b/notebooks/asian_barrier_option/README.md
@@ -0,0 +1,39 @@
+
+## Asian Barrier Options Pricing using GPU Acceleration
+
+### Introduction
+
+The European and American Options price can be estimated accurately by the efficient [Black–Scholes model](https://en.wikipedia.org/wiki/Black%E2%80%93Scholes_model). Options like [Barrier Option](https://en.wikipedia.org/wiki/Barrier_option) and [Basket Option](https://en.wikipedia.org/wiki/Basket_option) have a complicated structure with no simple analytical solution. The Monte Carlo simulation is an effective way to price them. To get an accurate price with a small variance, a large number of simulation paths are needed which is computationally intensive. Luckily, each of the simulation paths are independent and we can take advantage of the multiple core GPU to accelerate the computation. Using GPU can speedup the computation by orders of magnitude due to the parallelization of the independent paths. But even that is still not fast enough. Recently, [Deep learning derivatives method](https://arxiv.org/pdf/1809.02233.pdf) was introduced to value derivatives and achieves speedup even higher than the former.  
+
+In this tutorial, we are going to price the [Down-and-Out](https://www.investopedia.com/terms/d/daoo.asp) [Asian](https://www.investopedia.com/terms/a/asianoption.asp) [Barrier](https://www.investopedia.com/terms/b/barrieroption.asp) [Call Option](https://www.investopedia.com/terms/c/calloption.asp) :
+
+    
+### Barrier Option pricing
+
+Asian Barrier Option is a mixture of [Asian Option](https://en.wikipedia.org/wiki/Asian_option) and [Barrier Option](https://en.wikipedia.org/wiki/Barrier_option). The price depends on the average underlying Asset Price `S`, the Strick Price `K` and the Barrier Price `B`. There are 4 types of Barrier Options:-
+   * [Up-and-out](https://www.investopedia.com/terms/u/up-and-outoption.asp): spot price starts below the barrier level and has to move up for the option to be knocked out.
+   * [Down-and-out](https://www.investopedia.com/terms/d/daoo.asp): spot price starts above the barrier level and has to move down for the option to be knocked out.
+   * [Up-and-in](https://www.investopedia.com/terms/u/up-and-inoption.asp): spot price starts below the barrier level and has to move up for the option to become activated.
+   * [Down-and-in](https://www.investopedia.com/terms/d/daio.asp): spot price starts above the barrier level and has to move down for the option to become activated.
+
+Without loss of generality, in this tutorial we will use the [Down-and-Out Call Discretized Asian Barrier Option](https://ieeexplore.ieee.org/document/6327776/metrics#metrics) as an example. The option will be void if the average price of the underlying asset goes below the barrier. The asset Spot Price `S` is usually modeled as [Geometric Brownian motion](https://en.wikipedia.org/wiki/Geometric_Brownian_motion), which has 3 free parameters:- [Spot Price](https://www.investopedia.com/terms/s/spotprice.asp), [Percent Volatility](https://www.investopedia.com/terms/v/volatility.asp) and the [Percent Drift](https://en.wikipedia.org/wiki/Stochastic_drift). The price of the option will be the expected profit at the maturity discount to the current value.
+
+### Preliminary 
+
+You need to build a docker image to run the examples. 
+
+```bash
+cd docker
+build -f Dockerfile -t option .
+# launch your nvidia docker container and expose the port for Jupyterlab
+```
+
+### Outline 
+
+This tutorial is organized as following notebooks
+
+1. [Use Python GPU libraries to accelerate the Monte Carlo pricing on the GPU](./mc_pricing.ipynb)
+2. [Use the Monte Carlo pricing dynamic dataset to train an Option Pricing Neural Network Model](./deep_learning_option_1.ipynb)
+3. [Use the Monte Carlo pricing staic dataset to train an Option Pricing Neural Network Model and do inference](./deep_learning_option_2.ipynb)
+4. [Train an Asian Barrier Option Pricing Neural Network Model with NeMo](./deep_learning_nemo.ipynb)
+5. [Accelerate the Option Pricing Neural Network Model inference with TensorRT](./tensorrt.ipynb)
diff --git a/notebooks/asian_barrier_option/cuda_pricing.cu b/notebooks/asian_barrier_option/cuda_pricing.cu
new file mode 100644
index 00000000..9a2d1dc9
--- /dev/null
+++ b/notebooks/asian_barrier_option/cuda_pricing.cu
@@ -0,0 +1,190 @@
+#include <vector>
+#include <stdio.h>
+#include <iostream>
+#include <chrono>
+#include <cuda_runtime.h>
+#include <helper_cuda.h>
+#include <curand.h>
+ 
+#define CHECKCURAND(expression)                         \
+  {                                                     \
+    curandStatus_t status = (expression);                         \
+    if (status != CURAND_STATUS_SUCCESS) {                        \
+      std::cerr << "Curand Error on line " << __LINE__<< std::endl;     \
+      std::exit(EXIT_FAILURE);                                          \
+    }                                                                   \
+  }
+
+// atomicAdd is introduced for compute capability >=6.0
+#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 600
+#else
+__device__ double atomicAdd(double* address, double val)
+{
+      printf("device arch <=600\n");
+        unsigned long long int* address_as_ull = (unsigned long long int*)address;
+          unsigned long long int old = *address_as_ull, assumed;
+            do {
+                    assumed = old;
+                        old = atomicCAS(address_as_ull, assumed,
+                                                    __double_as_longlong(val + __longlong_as_double(assumed)));
+                          } while (assumed != old);
+              return __longlong_as_double(old);
+}
+#endif
+
+__global__ void sumPayoffKernel(float *d_s, const unsigned N_PATHS, double *mysum)
+{
+  unsigned idx =  threadIdx.x + blockIdx.x * blockDim.x;
+  unsigned stride = blockDim.x * gridDim.x;
+  unsigned tid = threadIdx.x;
+
+  extern __shared__ double smdata[];
+  smdata[tid] = 0.0;
+
+  for (unsigned i = idx; i<N_PATHS; i+=stride)
+  {
+    smdata[tid] += (double) d_s[i];
+  }
+
+  for (unsigned s=blockDim.x/2; s>0; s>>=1)
+  {
+    __syncthreads();
+    if (tid < s) smdata[tid] += smdata[tid + s];
+  }
+
+  if (tid == 0)
+  {
+    atomicAdd(mysum, smdata[0]);
+  }
+}
+
+__global__ void barrier_option(
+    float *d_s,
+    const float T,
+    const float K,
+    const float B,
+    const float S0,
+    const float sigma,
+    const float mu,
+    const float r,
+    const float * d_normals,
+    const long N_STEPS,
+    const long N_PATHS)
+{
+  unsigned idx =  threadIdx.x + blockIdx.x * blockDim.x;
+  unsigned stride = blockDim.x * gridDim.x;
+  const float tmp1 = mu*T/N_STEPS;
+  const float tmp2 = exp(-r*T);
+  const float tmp3 = sqrt(T/N_STEPS);
+  double running_average = 0.0;
+
+  for (unsigned i = idx; i<N_PATHS; i+=stride)
+  {
+    float s_curr = S0;
+    for(unsigned n = 0; n < N_STEPS; n++){
+       s_curr += tmp1 * s_curr + sigma*s_curr*tmp3*d_normals[i + n * N_PATHS];
+       running_average += (s_curr - running_average) / (n + 1.0) ;
+       if (running_average <= B){
+           break;
+       }
+    }
+
+    float payoff = (running_average>K ? running_average-K : 0.f);
+    d_s[i] = tmp2 * payoff;
+  }
+}
+
+int main(int argc, char *argv[]) {
+  try {
+    // declare variables and constants
+    size_t N_PATHS = 8192000;
+    size_t N_STEPS = 365;
+    if (argc >= 2)  N_PATHS = atoi(argv[1]);
+
+    if (argc >= 3)  N_STEPS = atoi(argv[2]);
+
+    const float T = 1.0f;
+    const float K = 110.0f;
+    const float B = 100.0f;
+    const float S0 = 120.0f;
+    const float sigma = 0.35f;
+    const float mu = 0.1f;
+    const float r = 0.05f;
+
+
+    double gpu_sum{0.0};
+
+    int devID{0};
+    cudaDeviceProp deviceProps;
+
+    checkCudaErrors(cudaGetDeviceProperties(&deviceProps, devID));
+    printf("CUDA device [%s]\n", deviceProps.name);
+    printf("GPU Device %d: \"%s\" with compute capability %d.%d\n\n", devID, deviceProps.name, deviceProps.major, deviceProps.minor);
+    // Generate random numbers on the device
+    curandGenerator_t curandGenerator;
+    CHECKCURAND(curandCreateGenerator(&curandGenerator, CURAND_RNG_PSEUDO_MTGP32));
+    CHECKCURAND(curandSetPseudoRandomGeneratorSeed(curandGenerator, 1234ULL)) ;
+
+    const size_t N_NORMALS = (size_t)N_STEPS * N_PATHS;
+    float *d_normals;
+    checkCudaErrors(cudaMalloc(&d_normals, N_NORMALS * sizeof(float)));
+    CHECKCURAND(curandGenerateNormal(curandGenerator, d_normals, N_NORMALS, 0.0f, 1.0f));
+    cudaDeviceSynchronize();
+
+  	// before kernel launch, check the max potential blockSize
+  	int BLOCK_SIZE, GRID_SIZE;
+  	checkCudaErrors(cudaOccupancyMaxPotentialBlockSize(&GRID_SIZE,
+  	                                                   &BLOCK_SIZE,
+  	                                                   barrier_option,
+  	                                                   0, N_PATHS));
+
+  	std::cout << "suggested block size " << BLOCK_SIZE
+  	          << " \nsuggested grid size " << GRID_SIZE
+  	          << std::endl;
+
+  	std::cout << "Used grid size " << GRID_SIZE << std::endl;
+
+  	// Kernel launch
+  	auto t1=std::chrono::high_resolution_clock::now();
+
+  	float *d_s;
+  	checkCudaErrors(cudaMalloc(&d_s, N_PATHS*sizeof(float)));
+
+  	auto t3=std::chrono::high_resolution_clock::now();
+  	barrier_option<<<GRID_SIZE, BLOCK_SIZE>>>(d_s, T, K, B, S0, sigma, mu, r, d_normals, N_STEPS, N_PATHS);
+  	cudaDeviceSynchronize();
+  	auto t4=std::chrono::high_resolution_clock::now();
+
+  	double* mySum;
+  	checkCudaErrors(cudaMallocManaged(&mySum, sizeof(double)));
+  	sumPayoffKernel<<<GRID_SIZE, BLOCK_SIZE, BLOCK_SIZE*sizeof(double)>>>(d_s, N_PATHS, mySum);
+  	cudaDeviceSynchronize();
+  	auto t5=std::chrono::high_resolution_clock::now();
+
+  	std::cout << "sumPayoffKernel takes "
+  	          << std::chrono::duration_cast<std::chrono::microseconds>(t5-t4).count() / 1000.f
+  	          << " ms\n";
+
+  	gpu_sum = mySum[0] / N_PATHS;
+
+  	auto t2=std::chrono::high_resolution_clock::now();
+
+  	// clean up
+  	CHECKCURAND(curandDestroyGenerator( curandGenerator )) ;
+  	checkCudaErrors(cudaFree(d_s));
+  	checkCudaErrors(cudaFree(d_normals));
+  	checkCudaErrors(cudaFree(mySum));
+
+  	std::cout << "price "
+              << gpu_sum
+              << " time "
+  	          << std::chrono::duration_cast<std::chrono::microseconds>(t5-t1).count() / 1000.f
+  	          << " ms\n";
+  }
+
+  catch(std::
+        exception& e)
+  {
+    std::cout<< "exception: " << e.what() << "\n";
+  }
+}
diff --git a/notebooks/asian_barrier_option/deep_learning_nemo.ipynb b/notebooks/asian_barrier_option/deep_learning_nemo.ipynb
new file mode 100644
index 00000000..31aba74c
--- /dev/null
+++ b/notebooks/asian_barrier_option/deep_learning_nemo.ipynb
@@ -0,0 +1,550 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train with NeMo\n",
+    "\n",
+    "[Neural Modules (NeMo)](https://nvidia.github.io/NeMo/index.html) is a framework-agnostic toolkit for building AI applications. It currently supports the PyTorch framework.\n",
+    "\n",
+    "Using NeMo to train a PyTorch model is simple. In this notebook, we will demonstrate how to use NeMo to train the Asian Barrier Option pricing model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Defining the trainable module is similar to defining a PyTorch module but it defines the input and output ports:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting nemo_model.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile nemo_model.py\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torch\n",
+    "from nemo.core.neural_types import BatchTag, ChannelTag, NeuralType, AxisType\n",
+    "import nemo\n",
+    "\n",
+    "class Net(nemo.backends.pytorch.nm.TrainableNM):\n",
+    "#class Net(nn.Module):\n",
+    "    @staticmethod\n",
+    "    def create_ports():\n",
+    "        input_ports = {\"x\": NeuralType({0: AxisType(BatchTag),\n",
+    "                                        1: AxisType(ChannelTag, 6)})}\n",
+    "        output_ports = {\"y_pred\": NeuralType({0: AxisType(BatchTag),\n",
+    "                                              1: AxisType(ChannelTag, 1)})}\n",
+    "        return input_ports, output_ports\n",
+    "\n",
+    "    def __init__(self, hidden=512, **kwargs):\n",
+    "        super(Net, self).__init__(**kwargs)\n",
+    "        self.fc1 = nn.Linear(6, hidden)\n",
+    "        self.fc2 = nn.Linear(hidden, hidden)\n",
+    "        self.fc3 = nn.Linear(hidden, hidden)\n",
+    "        self.fc4 = nn.Linear(hidden, hidden)\n",
+    "        self.fc5 = nn.Linear(hidden, hidden)\n",
+    "        self.fc6 = nn.Linear(hidden, 1)\n",
+    "        self.register_buffer('norm',\n",
+    "                             torch.tensor([200.0,\n",
+    "                                           198.0,\n",
+    "                                           200.0,\n",
+    "                                           0.4,\n",
+    "                                           0.2,\n",
+    "                                           0.2]))\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = x / self.norm\n",
+    "        x = F.elu(self.fc1(x))\n",
+    "        x = F.elu(self.fc2(x))\n",
+    "        x = F.elu(self.fc3(x))\n",
+    "        x = F.elu(self.fc4(x))\n",
+    "        x = F.elu(self.fc5(x))\n",
+    "        return self.fc6(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The NeMo DataLayer module is wrapped around the normal PyTorch Dataset:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing nemo_datalayer.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile nemo_datalayer.py\n",
+    "import torch\n",
+    "import nemo\n",
+    "from nemo.core.neural_types import BatchTag, ChannelTag, NeuralType, AxisType\n",
+    "\n",
+    "\n",
+    "class OptionDataSet(torch.utils.data.Dataset):\n",
+    "    def __init__(self, filename, rank=0, world_size=5):\n",
+    "        tensor = torch.load(filename)\n",
+    "        self.tensor = (tensor[0], tensor[1])\n",
+    "        self.length = len(self.tensor[0]) // world_size\n",
+    "        self.world_size = world_size\n",
+    "        self.rank = rank\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        index = index * self.world_size + self.rank\n",
+    "\n",
+    "        return self.tensor[0][index], self.tensor[1][index]\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return self.length\n",
+    "\n",
+    "class OptionDataLayer(nemo.backends.pytorch.nm.DataLayerNM):\n",
+    "    @staticmethod\n",
+    "    def create_ports():\n",
+    "        # Note: we define the size of the height and width of our output\n",
+    "        # tensors, and thus require a size parameter.\n",
+    "        input_ports = {}\n",
+    "        output_ports = {\n",
+    "            \"x\": NeuralType({0: AxisType(BatchTag),\n",
+    "                                 1: AxisType(ChannelTag, 6)}),\n",
+    "            \"ground\": NeuralType({0: AxisType(BatchTag)})\n",
+    "        }\n",
+    "        return input_ports, output_ports\n",
+    "\n",
+    "    def __init__(self, filename, rank=0, world_size=5, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self._dataset = OptionDataSet(filename, rank, world_size)\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self._dataset)\n",
+    "\n",
+    "    @property\n",
+    "    def dataset(self):\n",
+    "        return self._dataset\n",
+    "\n",
+    "    @property\n",
+    "    def data_iterator(self):\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We define the Loss Neural Module as following, which wraps around the PyTorch MSELoss with added input and output types:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting nemo_losslayer.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile nemo_losslayer.py\n",
+    "import nemo\n",
+    "from nemo.core.neural_types import BatchTag, ChannelTag, NeuralType, AxisType\n",
+    "import torch\n",
+    "\n",
+    "class MSELoss(nemo.backends.pytorch.nm.LossNM):\n",
+    "    @staticmethod\n",
+    "    def create_ports():\n",
+    "        input_ports = {\"y_pred\": NeuralType({0: AxisType(BatchTag),\n",
+    "                                             1: AxisType(ChannelTag, 1)}),\n",
+    "                       \"ground\": NeuralType({0: AxisType(BatchTag)})}\n",
+    "        output_ports = {\"loss\": NeuralType(None)}\n",
+    "        return input_ports, output_ports\n",
+    "\n",
+    "    def __init__(self, **kwargs):\n",
+    "        # Neural Module API specific\n",
+    "        super().__init__(**kwargs)\n",
+    "        # End of Neural Module API specific\n",
+    "        self._loss = torch.nn.MSELoss()\n",
+    "\n",
+    "    # You need to implement this function\n",
+    "    def _loss_function(self, **kwargs):\n",
+    "        v = self._loss(kwargs['y_pred'][:,0], kwargs['ground'])\n",
+    "        return v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use Neural Modules, we need to following 3 steps:-\n",
+    "\n",
+    "1. Creation of NeuralModuleFactory and necessary NeuralModule\n",
+    "2. Defining a Directed Acyclic Graph (DAG) of NeuralModule\n",
+    "3. Call to “action” such as train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2019-11-18 21:52:15,176 - WARNING - Data Layer does not have any weights to return. This get_weights call returns None.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting .....\n",
+      "Starting epoch 0\n",
+      "Step: 0\n",
+      "Train Loss: 2603.74560546875\n",
+      "Step time: 0.34972667694091797 seconds\n",
+      "Finished epoch 0 in 1.576570987701416\n",
+      "Starting epoch 1\n",
+      "Step: 25\n",
+      "Train Loss: 2615.87451171875\n",
+      "Step time: 0.0040209293365478516 seconds\n",
+      "Finished epoch 1 in 1.1546299457550049\n",
+      "Starting epoch 2\n",
+      "Step: 50\n",
+      "Train Loss: 374.9800109863281\n",
+      "Step time: 0.004566669464111328 seconds\n",
+      "Finished epoch 2 in 1.1191346645355225\n",
+      "Starting epoch 3\n",
+      "Finished epoch 3 in 1.0970311164855957\n",
+      "Starting epoch 4\n",
+      "Step: 75\n",
+      "Train Loss: 39.58891677856445\n",
+      "Step time: 0.0044879913330078125 seconds\n",
+      "Finished epoch 4 in 1.1738121509552002\n",
+      "Starting epoch 5\n",
+      "Step: 100\n",
+      "Train Loss: 9.877204895019531\n",
+      "Step time: 0.004233360290527344 seconds\n",
+      "Finished epoch 5 in 1.15018892288208\n",
+      "Starting epoch 6\n",
+      "Step: 125\n",
+      "Train Loss: 1.8599201440811157\n",
+      "Step time: 0.005522727966308594 seconds\n",
+      "Finished epoch 6 in 1.2052836418151855\n",
+      "Starting epoch 7\n",
+      "Finished epoch 7 in 1.197835922241211\n",
+      "Starting epoch 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "import nemo\n",
+    "from nemo.core import DeviceType\n",
+    "from nemo_model import Net\n",
+    "from nemo_datalayer import OptionDataLayer\n",
+    "from nemo_losslayer import MSELoss\n",
+    "nf = nemo.core.NeuralModuleFactory()\n",
+    "# nf = nemo.core.NeuralModuleFactory()\n",
+    "dl= OptionDataLayer('trn.pth', 0, 1, batch_size=32)\n",
+    "\n",
+    "# instantiate necessary neural modules\n",
+    "fx = Net(hidden=512).cuda() #, placement=DeviceType.GPU)\n",
+    "loss = MSELoss()\n",
+    "\n",
+    "# describe activation's flow\n",
+    "x, y = dl()\n",
+    "p = fx(x=x)\n",
+    "lss = loss(y_pred=p, ground=y)\n",
+    "\n",
+    "# SimpleLossLoggerCallback will print loss values to console.\n",
+    "callback = nemo.core.SimpleLossLoggerCallback(\n",
+    "    tensors=[lss],\n",
+    "    print_func=lambda x: print(f'Train Loss: {str(x[0].item())}'))\n",
+    "\n",
+    "# Invoke \"train\" action\n",
+    "nf.train([lss], callbacks=[callback],\n",
+    "         optimization_params={\"num_epochs\": 20, \"lr\": 0.0003},\n",
+    "         optimizer=\"adam\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NVIDIA Volta and Turing GPUs have Tensor Cores which can do fast matrix multiplications with values in float16 format. To enable mixed-precision in NeMo all you need to do is to set the optimization_level parameter of nemo.core.NeuralModuleFactory to nemo.core.Optimization.mxprO1. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nf = nemo.core.NeuralModuleFactory(optimization_level=nemo.core.Optimization.mxprO1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For multi-GPU training, follow two steps in NeMo:\n",
+    "1. Set placement to nemo.core.DeviceType.AllGpu in NeuralModuleFactory\n",
+    "2. Add the ‘local_rank’ argument to your script and do not set it yourself: parser.add_argument(“–local_rank”, default=None, type=int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting nemo_dis_train.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile nemo_dis_train.py\n",
+    "import nemo\n",
+    "from nemo.core import DeviceType\n",
+    "from nemo_model import Net\n",
+    "from nemo_datalayer import OptionDataLayer\n",
+    "from nemo_losslayer import MSELoss\n",
+    "import argparse\n",
+    "import os\n",
+    "parser = argparse.ArgumentParser(description='ResNet50 on ImageNet')\n",
+    "parser.add_argument(\"--local_rank\", default=None, type=int)\n",
+    "\n",
+    "args = parser.parse_args()\n",
+    "\n",
+    "if args.local_rank is not None:\n",
+    "    device = nemo.core.DeviceType.AllGpu\n",
+    "else:\n",
+    "    device = nemo.core.DeviceType.GPU\n",
+    "    \n",
+    "world_size = int(os.environ['WORLD_SIZE'])\n",
+    "\n",
+    "nf = nemo.core.NeuralModuleFactory(backend=nemo.core.Backend.PyTorch,\n",
+    "    local_rank=args.local_rank,\n",
+    "    placement=device,                               \n",
+    "    optimization_level=nemo.core.Optimization.mxprO1)\n",
+    "# nf = nemo.core.NeuralModuleFactory()\n",
+    "dl= OptionDataLayer('trn.pth', args.local_rank, world_size, batch_size=32)\n",
+    "\n",
+    "# instantiate necessary neural modules\n",
+    "# RealFunctionDataLayer defaults to f=torch.sin, sampling from x=[-4, 4]\n",
+    "fx = Net(hidden=512).cuda() #, placement=DeviceType.GPU)\n",
+    "loss = MSELoss()\n",
+    "\n",
+    "# describe activation's flow\n",
+    "x, y = dl()\n",
+    "p = fx(x=x)\n",
+    "lss = loss(y_pred=p, ground=y)\n",
+    "\n",
+    "# SimpleLossLoggerCallback will print loss values to console.\n",
+    "callback = nemo.core.SimpleLossLoggerCallback(\n",
+    "    tensors=[lss],\n",
+    "    print_func=lambda x: print(f'Train Loss: {str(x[0].item())}'))\n",
+    "\n",
+    "# Invoke \"train\" action\n",
+    "nf.train([lss], callbacks=[callback],\n",
+    "         optimization_params={\"num_epochs\": 20, \"lr\": 0.0003},\n",
+    "         optimizer=\"adam\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*****************************************\n",
+      "Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. \n",
+      "*****************************************\n",
+      "WARNING:root:Data Layer does not have any weights to return. This get_weights call returns None.\n",
+      "Doing distributed training\n",
+      "WARNING:root:Data Layer does not have any weights to return. This get_weights call returns None.\n",
+      "Doing distributed training\n",
+      "WARNING:root:Data Layer does not have any weights to return. This get_weights call returns None.\n",
+      "Doing distributed training\n",
+      "2019-11-18 21:59:47,135 - WARNING - Data Layer does not have any weights to return. This get_weights call returns None.\n",
+      "Selected optimization level O1:  Insert automatic casts around Pytorch functions and Tensor methods.\n",
+      "\n",
+      "Defaults for this optimization level are:\n",
+      "enabled                : True\n",
+      "opt_level              : O1\n",
+      "cast_model_type        : None\n",
+      "patch_torch_functions  : True\n",
+      "keep_batchnorm_fp32    : None\n",
+      "master_weights         : None\n",
+      "loss_scale             : dynamic\n",
+      "Processing user overrides (additional kwargs that are not None)...\n",
+      "After processing overrides, optimization options are:\n",
+      "enabled                : True\n",
+      "opt_level              : O1\n",
+      "cast_model_type        : None\n",
+      "patch_torch_functions  : True\n",
+      "keep_batchnorm_fp32    : None\n",
+      "master_weights         : None\n",
+      "loss_scale             : dynamic\n",
+      "Doing distributed training\n",
+      "Starting .....\n",
+      "Starting epoch 0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 32768.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 32768.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 32768.0\n",
+      "Step: 0\n",
+      "Train Loss: 3221.57763671875\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 32768.0\n",
+      "Step time: 0.6914923191070557 seconds\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 16384.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 16384.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 16384.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 16384.0\n",
+      "Finished epoch 0 in 1.6740753650665283\n",
+      "Starting epoch 1\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 8192.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 8192.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 8192.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 8192.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 4096.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 4096.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 4096.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 4096.0\n",
+      "Finished epoch 1 in 1.2843654155731201\n",
+      "Starting epoch 2\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 2048.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 2048.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 2048.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 2048.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 1024.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 1024.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 1024.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 1024.0\n",
+      "Finished epoch 2 in 1.2458338737487793\n",
+      "Starting epoch 3\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 512.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 512.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 512.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 512.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 256.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 256.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 256.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 256.0\n",
+      "Finished epoch 3 in 1.2108018398284912\n",
+      "Starting epoch 4\n",
+      "Finished epoch 4 in 1.2647509574890137\n",
+      "Starting epoch 5\n",
+      "Finished epoch 5 in 1.2803404331207275\n",
+      "Starting epoch 6\n",
+      "Finished epoch 6 in 1.2259752750396729\n",
+      "Starting epoch 7\n",
+      "Finished epoch 7 in 1.1974444389343262\n",
+      "Starting epoch 8\n",
+      "Finished epoch 8 in 1.198972225189209\n",
+      "Starting epoch 9\n",
+      "Finished epoch 9 in 1.2113327980041504\n",
+      "Starting epoch 10\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 128.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 128.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 128.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 128.0\n",
+      "Finished epoch 10 in 1.2995426654815674\n",
+      "Starting epoch 11\n",
+      "Finished epoch 11 in 1.2914905548095703\n",
+      "Starting epoch 12\n",
+      "Step: 25\n",
+      "Train Loss: 995.6943969726562\n",
+      "Step time: 0.00768280029296875 seconds\n",
+      "Finished epoch 12 in 1.258274793624878\n",
+      "Starting epoch 13\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 64.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 64.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 64.0\n",
+      "Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 64.0\n",
+      "Finished epoch 13 in 1.2511169910430908\n",
+      "Starting epoch 14\n",
+      "Finished epoch 14 in 1.2451517581939697\n",
+      "Starting epoch 15\n",
+      "Finished epoch 15 in 1.2092945575714111\n",
+      "Starting epoch 16\n",
+      "Finished epoch 16 in 1.2507727146148682\n",
+      "Starting epoch 17\n",
+      "Finished epoch 17 in 1.2585015296936035\n",
+      "Starting epoch 18\n",
+      "Finished epoch 18 in 1.2153122425079346\n",
+      "Starting epoch 19\n",
+      "Finished epoch 19 in 1.230492353439331\n",
+      "Done in 25.3052020072937\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python -m torch.distributed.launch --nproc_per_node=4 nemo_dis_train.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [callback API](https://nvidia.github.io/NeMo/tutorials/callbacks.html) makes setting up check points and evaluating the validation dataset easy. Interested readers please check the document for details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/asian_barrier_option/deep_learning_option_1.ipynb b/notebooks/asian_barrier_option/deep_learning_option_1.ipynb
new file mode 100644
index 00000000..33161dcd
--- /dev/null
+++ b/notebooks/asian_barrier_option/deep_learning_option_1.ipynb
@@ -0,0 +1,883 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deep Learning Barrier Option\n",
+    "\n",
+    "We used Numba and CuPy in the previous notebook to run Monte Carlo simulation to determine the price of the Asian Barrier option. A Monte Carlo simulation needs millions of paths to get an accurate answer which is computationally intensive. [Ryan et al (2018)](https://arxiv.org/abs/1809.02233) showed that a deep learning model can be trained to value derivatives. The deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models. In the this notebook, we will use a fully connected network to learn the pricing mode of the Asian Barrier option. Monte Carlo simulation is used as pricing ground truth for the training. We use the same Asian Barrier Option model as last notebook with parameters listed as following:\n",
+    "\n",
+    "```\n",
+    "T - Maturity (yrs.)\n",
+    "S - Spot (usd)\n",
+    "K - Strike (usd)\n",
+    "sigma - Volatility (per.)\n",
+    "r - Risk Free Rate (per.)\n",
+    "mu - Stock Drift Rate (per.)\n",
+    "B - Barrier (usd)\n",
+    "```\n",
+    "\n",
+    "### Batched Data generation\n",
+    "\n",
+    "The dataset is an important part of the Deep learning training. We will modify the previous single Asian Barrier Option pricing code to handle a batch of Barrier Option pricing. \n",
+    "\n",
+    "Loading all the necessary libraries:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cupy\n",
+    "import numpy as np\n",
+    "import math\n",
+    "import time\n",
+    "import torch\n",
+    "cupy.cuda.set_allocator(None)\n",
+    "from torch.utils.dlpack import from_dlpack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The CuPy version of batched barrier option pricing simulation is as follows:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cupy_batched_barrier_option = cupy.RawKernel(r'''\n",
+    "extern \"C\" __global__ void batched_barrier_option(\n",
+    "    float *d_s,\n",
+    "    const float T,\n",
+    "    const float * K,\n",
+    "    const float * B,\n",
+    "    const float * S0,\n",
+    "    const float * sigma,\n",
+    "    const float * mu,\n",
+    "    const float * r,\n",
+    "    const float * d_normals,\n",
+    "    const long N_STEPS,\n",
+    "    const long N_PATHS,\n",
+    "    const long N_BATCH)\n",
+    "{\n",
+    "  unsigned idx =  threadIdx.x + blockIdx.x * blockDim.x;\n",
+    "  unsigned stride = blockDim.x * gridDim.x;\n",
+    "  unsigned tid = threadIdx.x;\n",
+    "  const float tmp3 = sqrt(T/N_STEPS);\n",
+    "\n",
+    "\n",
+    "  for (unsigned i = idx; i<N_PATHS * N_BATCH; i+=stride)\n",
+    "  {\n",
+    "    int batch_id = i / N_PATHS;\n",
+    "    int path_id = i % N_PATHS;\n",
+    "    float s_curr = S0[batch_id];\n",
+    "    float tmp1 = mu[batch_id]*T/N_STEPS;\n",
+    "    float tmp2 = exp(-r[batch_id]*T);\n",
+    "    unsigned n=0;\n",
+    "    double running_average = 0.0;\n",
+    "    for(unsigned n = 0; n < N_STEPS; n++){\n",
+    "       s_curr += tmp1 * s_curr + sigma[batch_id]*s_curr*tmp3*d_normals[path_id + batch_id * N_PATHS + n * N_PATHS * N_BATCH];\n",
+    "       running_average += (s_curr - running_average) / (n + 1.0);\n",
+    "       if (running_average <= B[batch_id]){\n",
+    "           break;\n",
+    "       }\n",
+    "    }\n",
+    "\n",
+    "    float payoff = (running_average>K[batch_id] ? running_average-K[batch_id] : 0.f); \n",
+    "    d_s[i] = tmp2 * payoff;\n",
+    "  }\n",
+    "}\n",
+    "\n",
+    "''', 'batched_barrier_option')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note, the parameters (K, B, S0, sigma, mu, r) are passed in as an array with length of batch size. The output array is a two dimensional array flatten to 1-D. The first dimension is for Batch and the second dimension is for Path. \n",
+    "\n",
+    "Testing it out by entering two sets of option parameters:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N_PATHS = 2048000\n",
+    "N_STEPS = 365\n",
+    "N_BATCH = 2\n",
+    "T = 1.0\n",
+    "\n",
+    "K = cupy.array([110.0, 120.0], dtype=cupy.float32)\n",
+    "B = cupy.array([100.0, 90.0], dtype=cupy.float32)\n",
+    "S0 = cupy.array([120.0, 100.0], dtype=cupy.float32)\n",
+    "sigma = cupy.array([0.35, 0.2], dtype=cupy.float32)\n",
+    "mu = cupy.array([0.15, 0.1], dtype=cupy.float32)\n",
+    "r =cupy.array([0.05, 0.05], dtype=cupy.float32)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Put everything into a simple function to launch this GPU kernel. The option prices for each batch is the average of the corresponding path terminal values. This can be computed easily by Cupy function `mean(axis=1)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time 0.013919591903686523 v [21.22405    0.8480416]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def batch_run():\n",
+    "    number_of_threads = 256\n",
+    "    number_of_blocks = (N_PATHS * N_BATCH - 1) // number_of_threads + 1\n",
+    "    randoms_gpu = cupy.random.normal(0, 1, N_BATCH*N_PATHS * N_STEPS, dtype=cupy.float32)\n",
+    "    output = cupy.zeros(N_BATCH*N_PATHS, dtype=cupy.float32)\n",
+    "    cupy.cuda.stream.get_current_stream().synchronize()\n",
+    "    s = time.time()\n",
+    "    cupy_batched_barrier_option((number_of_blocks,), (number_of_threads,),\n",
+    "                       (output, np.float32(T), K, B, S0, sigma, mu, r,\n",
+    "                        randoms_gpu, N_STEPS, N_PATHS, N_BATCH))\n",
+    "    v = output.reshape(N_BATCH, N_PATHS).mean(axis=1)\n",
+    "    cupy.cuda.stream.get_current_stream().synchronize()\n",
+    "    e = time.time()\n",
+    "    print('time', e-s, 'v',v)\n",
+    "batch_run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This produces the option prices $21.22$ and $0.848$ for these two sets of option parameters in $66ms$.\n",
+    "\n",
+    "It works efficiently hence we will construct an `OptionDataSet` class to wrap the above code so we can use it in Pytorch. For every `next` element, it generates uniform distributed random option parameters in the specified range, launches the GPU kernel to compute the option prices, convert the CuPy array to Pytorch tensors with zero copy via the DLPack. Note how we implemented the iterable Dataset interface:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class OptionDataSet(torch.utils.data.IterableDataset):\n",
+    "    \n",
+    "    def __init__(self, max_len=10, number_path = 1000, batch=2, threads=256,seed=15):\n",
+    "        self.num = 0\n",
+    "        self.max_length = max_len\n",
+    "        self.N_PATHS = number_path\n",
+    "        self.N_STEPS = 365\n",
+    "        self.N_BATCH = batch\n",
+    "        self.T = np.float32(1.0)\n",
+    "        self.output = cupy.zeros(self.N_BATCH*self.N_PATHS, dtype=cupy.float32) \n",
+    "        self.number_of_blocks = (self.N_PATHS * self.N_BATCH - 1) // threads + 1\n",
+    "        self.number_of_threads = threads\n",
+    "        cupy.random.seed(seed)\n",
+    "        \n",
+    "    def __len__(self):\n",
+    "        return self.max_length\n",
+    "        \n",
+    "    def __iter__(self):\n",
+    "        self.num = 0\n",
+    "        return self\n",
+    "    \n",
+    "    def __next__(self):\n",
+    "        if self.num > self.max_length:\n",
+    "            raise StopIteration\n",
+    "        X = cupy.random.rand(self.N_BATCH, 6, dtype=cupy.float32)\n",
+    "        # scale the [0, 1) random numbers to the correct range for each of the option parameters\n",
+    "        X = X * cupy.array([200.0, 0.99, 200.0, 0.4, 0.2, 0.2], dtype=cupy.float32)\n",
+    "        # make sure the Barrier is smaller than the Strike price\n",
+    "        X[:, 1] = X[:, 0] * X[:, 1]\n",
+    "        randoms = cupy.random.normal(0, 1, self.N_BATCH * self.N_PATHS * self.N_STEPS, dtype=cupy.float32)\n",
+    "        cupy_batched_barrier_option((self.number_of_blocks,), (self.number_of_threads,), (self.output, self.T, cupy.ascontiguousarray(X[:, 0]), \n",
+    "                              cupy.ascontiguousarray(X[:, 1]), cupy.ascontiguousarray(X[:, 2]), cupy.ascontiguousarray(X[:, 3]), cupy.ascontiguousarray(X[:, 4]), cupy.ascontiguousarray(X[:, 5]), randoms, self.N_STEPS, self.N_PATHS, self.N_BATCH))\n",
+    "        Y = self.output.reshape(self.N_BATCH, self.N_PATHS).mean(axis=1)\n",
+    "        self.num += 1\n",
+    "        return (from_dlpack(X.toDlpack()), from_dlpack(Y.toDlpack()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Put everything related to Pytorch dataset into a file `cupy_dataset.py`:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting cupy_dataset.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile cupy_dataset.py \n",
+    "import cupy\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "from torch.utils.dlpack import from_dlpack\n",
+    "cupy.cuda.set_allocator(None)\n",
+    "\n",
+    "cupy_batched_barrier_option = cupy.RawKernel(r'''\n",
+    "extern \"C\" __global__ void batched_barrier_option(\n",
+    "    float *d_s,\n",
+    "    const float T,\n",
+    "    const float * K,\n",
+    "    const float * B,\n",
+    "    const float * S0,\n",
+    "    const float * sigma,\n",
+    "    const float * mu,\n",
+    "    const float * r,\n",
+    "    const float * d_normals,\n",
+    "    const long N_STEPS,\n",
+    "    const long N_PATHS,\n",
+    "    const long N_BATCH)\n",
+    "{\n",
+    "  unsigned idx =  threadIdx.x + blockIdx.x * blockDim.x;\n",
+    "  unsigned stride = blockDim.x * gridDim.x;\n",
+    "  unsigned tid = threadIdx.x;\n",
+    "  const float tmp3 = sqrt(T/N_STEPS);\n",
+    "\n",
+    "\n",
+    "  for (unsigned i = idx; i<N_PATHS * N_BATCH; i+=stride)\n",
+    "  {\n",
+    "    int batch_id = i / N_PATHS;\n",
+    "    int path_id = i % N_PATHS;\n",
+    "    float s_curr = S0[batch_id];\n",
+    "    float tmp1 = mu[batch_id]*T/N_STEPS;\n",
+    "    float tmp2 = exp(-r[batch_id]*T);\n",
+    "    unsigned n=0;\n",
+    "    double running_average = 0.0;\n",
+    "    for(unsigned n = 0; n < N_STEPS; n++){\n",
+    "       s_curr += tmp1 * s_curr + sigma[batch_id]*s_curr*tmp3*d_normals[path_id + batch_id * N_PATHS + n * N_PATHS * N_BATCH];\n",
+    "       running_average += (s_curr - running_average) / (n + 1.0);\n",
+    "       if (running_average <= B[batch_id]){\n",
+    "           break;\n",
+    "       }\n",
+    "    }\n",
+    "\n",
+    "    float payoff = (running_average>K[batch_id] ? running_average-K[batch_id] : 0.f); \n",
+    "    d_s[i] = tmp2 * payoff;\n",
+    "  }\n",
+    "}\n",
+    "\n",
+    "''', 'batched_barrier_option')\n",
+    "\n",
+    "class OptionDataSet(torch.utils.data.IterableDataset):\n",
+    "    \n",
+    "    def __init__(self, max_len=10, number_path = 1000, batch=2, threads=256,seed=15):\n",
+    "        self.num = 0\n",
+    "        self.max_length = max_len\n",
+    "        self.N_PATHS = number_path\n",
+    "        self.N_STEPS = 365\n",
+    "        self.N_BATCH = batch\n",
+    "        self.T = np.float32(1.0)\n",
+    "        self.output = cupy.zeros(self.N_BATCH*self.N_PATHS, dtype=cupy.float32) \n",
+    "        self.number_of_blocks = (self.N_PATHS * self.N_BATCH - 1) // threads + 1\n",
+    "        self.number_of_threads = threads\n",
+    "        cupy.random.seed(seed)\n",
+    "        \n",
+    "    def __len__(self):\n",
+    "        return self.max_length\n",
+    "        \n",
+    "    def __iter__(self):\n",
+    "        self.num = 0\n",
+    "        return self\n",
+    "    \n",
+    "    def __next__(self):\n",
+    "        if self.num > self.max_length:\n",
+    "            raise StopIteration\n",
+    "        X = cupy.random.rand(self.N_BATCH, 6, dtype=cupy.float32)\n",
+    "        # scale the [0, 1) random numbers to the correct range for each of the option parameters\n",
+    "        X = X * cupy.array([200.0, 0.99, 200.0, 0.4, 0.2, 0.2], dtype=cupy.float32)\n",
+    "        # make sure the Barrier is smaller than the Strike price\n",
+    "        X[:, 1] = X[:, 0] * X[:, 1]\n",
+    "        randoms = cupy.random.normal(0, 1, self.N_BATCH * self.N_PATHS * self.N_STEPS, dtype=cupy.float32)\n",
+    "        cupy_batched_barrier_option((self.number_of_blocks,), (self.number_of_threads,), (self.output, self.T, cupy.ascontiguousarray(X[:, 0]), \n",
+    "                              cupy.ascontiguousarray(X[:, 1]), cupy.ascontiguousarray(X[:, 2]), cupy.ascontiguousarray(X[:, 3]), cupy.ascontiguousarray(X[:, 4]), cupy.ascontiguousarray(X[:, 5]), randoms, self.N_STEPS, self.N_PATHS, self.N_BATCH))\n",
+    "        Y = self.output.reshape(self.N_BATCH, self.N_PATHS).mean(axis=1)\n",
+    "        self.num += 1\n",
+    "        return (from_dlpack(X.toDlpack()), from_dlpack(Y.toDlpack()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is a test code to sample 10 data points with batch size 16:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([1.6558e+02, 0.0000e+00, 8.0069e+01, 1.0866e+02, 7.7740e-03, 0.0000e+00,\n",
+      "        2.7772e+01, 0.0000e+00, 0.0000e+00, 6.4279e+01, 0.0000e+00, 5.1346e+00,\n",
+      "        0.0000e+00, 1.4733e+02, 4.1851e+01, 0.0000e+00], device='cuda:0')\n",
+      "tensor([ 57.1285,   0.0000,   0.0000, 151.9433,   0.0000,   0.0000,   0.0000,\n",
+      "          9.3306,   0.0000,   0.7246, 157.0885,  10.7096,   0.0000,   0.7067,\n",
+      "         59.1110,  14.6442], device='cuda:0')\n",
+      "tensor([106.4531,   0.0000,  51.1248,  12.7823,  67.4821,   0.0000,   7.3539,\n",
+      "          0.0000, 143.2203,  66.0655,  66.5477, 129.6811,   0.0000,  13.5559,\n",
+      "         27.5546,   0.0000], device='cuda:0')\n",
+      "tensor([4.1777e+01, 0.0000e+00, 2.5890e+00, 1.4500e+02, 0.0000e+00, 1.5099e+00,\n",
+      "        1.1183e+02, 5.6967e+01, 7.5750e-05, 1.2390e+01, 0.0000e+00, 3.0183e+01,\n",
+      "        1.3890e+01, 5.0533e+01, 3.8499e+01, 8.2232e+01], device='cuda:0')\n",
+      "tensor([1.0687e+02, 3.0590e+01, 8.5428e+01, 1.9835e+01, 3.0602e+01, 1.5230e+00,\n",
+      "        0.0000e+00, 0.0000e+00, 4.0244e+01, 0.0000e+00, 3.7487e-01, 0.0000e+00,\n",
+      "        1.1777e+02, 0.0000e+00, 9.6200e+00, 4.2073e-04], device='cuda:0')\n",
+      "tensor([ 83.6088, 125.8481,   0.0000,   0.0000,   0.0000,  35.1237,  26.4887,\n",
+      "        114.6908,   1.2338, 133.6484,  84.3443,  49.0381,  33.3620,  93.0905,\n",
+      "         40.8572,  30.2684], device='cuda:0')\n",
+      "tensor([1.6068e+01, 6.8251e+01, 1.7516e+00, 6.3889e+01, 2.0682e+00, 3.0282e-01,\n",
+      "        2.3074e-04, 2.4942e+01, 1.1639e+02, 0.0000e+00, 3.0597e+01, 0.0000e+00,\n",
+      "        3.0390e+01, 2.1144e+00, 8.2769e-04, 6.3105e+01], device='cuda:0')\n",
+      "tensor([129.0360,   0.0000,   0.0000,  34.7129,  76.3240,  61.5014,  96.1047,\n",
+      "         41.5991,   0.0000,   0.0000,   1.6868,   0.0000,   0.0000, 198.8765,\n",
+      "          0.0000, 130.8935], device='cuda:0')\n",
+      "tensor([23.4824, 49.1953, 70.5731,  0.0000,  0.0000, 35.5231,  0.0000,  0.0000,\n",
+      "         0.0000, 64.7130,  0.0000, 56.6821,  3.6377,  0.0000,  0.0000, 17.6415],\n",
+      "       device='cuda:0')\n",
+      "tensor([113.4123,   0.2840,   0.0000,   9.8790,  34.9789,  62.0461,   0.0000,\n",
+      "          0.0000,  90.4281, 151.8807,   0.0000,   0.0000,  75.6426, 137.9153,\n",
+      "          0.0000,  65.4237], device='cuda:0')\n",
+      "tensor([1.1853e+02, 0.0000e+00, 0.0000e+00, 3.5182e+01, 8.2466e+01, 0.0000e+00,\n",
+      "        0.0000e+00, 1.7089e+01, 0.0000e+00, 2.8777e-02, 0.0000e+00, 0.0000e+00,\n",
+      "        6.7766e+01, 3.9360e+01, 1.2019e+02, 1.0623e+02], device='cuda:0')\n"
+     ]
+    }
+   ],
+   "source": [
+    "from cupy_dataset import OptionDataSet\n",
+    "ds = OptionDataSet(10, number_path=100000, batch=16, seed=15)\n",
+    "for i in ds:\n",
+    "    print(i[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can implement the same code by using Numba to accelerate the calculation in GPU:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([1.6558e+02, 0.0000e+00, 8.0069e+01, 1.0866e+02, 7.7740e-03, 0.0000e+00,\n",
+      "        2.7772e+01, 0.0000e+00, 0.0000e+00, 6.4279e+01, 0.0000e+00, 5.1346e+00,\n",
+      "        0.0000e+00, 1.4733e+02, 4.1851e+01, 0.0000e+00], device='cuda:0')\n",
+      "tensor([ 57.1285,   0.0000,   0.0000, 151.9433,   0.0000,   0.0000,   0.0000,\n",
+      "          9.3306,   0.0000,   0.7246, 157.0885,  10.7096,   0.0000,   0.7067,\n",
+      "         59.1110,  14.6442], device='cuda:0')\n",
+      "tensor([106.4531,   0.0000,  51.1248,  12.7823,  67.4821,   0.0000,   7.3539,\n",
+      "          0.0000, 143.2203,  66.0655,  66.5476, 129.6811,   0.0000,  13.5559,\n",
+      "         27.5546,   0.0000], device='cuda:0')\n",
+      "tensor([4.1777e+01, 0.0000e+00, 2.5890e+00, 1.4500e+02, 0.0000e+00, 1.5099e+00,\n",
+      "        1.1183e+02, 5.6967e+01, 7.5751e-05, 1.2390e+01, 0.0000e+00, 3.0183e+01,\n",
+      "        1.3890e+01, 5.0533e+01, 3.8499e+01, 8.2232e+01], device='cuda:0')\n",
+      "tensor([1.0687e+02, 3.0590e+01, 8.5428e+01, 1.9835e+01, 3.0602e+01, 1.5230e+00,\n",
+      "        0.0000e+00, 0.0000e+00, 4.0244e+01, 0.0000e+00, 3.7487e-01, 0.0000e+00,\n",
+      "        1.1777e+02, 0.0000e+00, 9.6200e+00, 4.2073e-04], device='cuda:0')\n",
+      "tensor([ 83.6088, 125.8481,   0.0000,   0.0000,   0.0000,  35.1237,  26.4887,\n",
+      "        114.6908,   1.2338, 133.6484,  84.3443,  49.0380,  33.3620,  93.0905,\n",
+      "         40.8572,  30.2684], device='cuda:0')\n",
+      "tensor([1.6068e+01, 6.8251e+01, 1.7516e+00, 6.3889e+01, 2.0682e+00, 3.0282e-01,\n",
+      "        2.3074e-04, 2.4942e+01, 1.1639e+02, 0.0000e+00, 3.0597e+01, 0.0000e+00,\n",
+      "        3.0390e+01, 2.1144e+00, 8.2769e-04, 6.3105e+01], device='cuda:0')\n",
+      "tensor([129.0360,   0.0000,   0.0000,  34.7129,  76.3240,  61.5014,  96.1047,\n",
+      "         41.5991,   0.0000,   0.0000,   1.6868,   0.0000,   0.0000, 198.8765,\n",
+      "          0.0000, 130.8935], device='cuda:0')\n",
+      "tensor([23.4824, 49.1953, 70.5731,  0.0000,  0.0000, 35.5231,  0.0000,  0.0000,\n",
+      "         0.0000, 64.7129,  0.0000, 56.6821,  3.6377,  0.0000,  0.0000, 17.6415],\n",
+      "       device='cuda:0')\n",
+      "tensor([113.4123,   0.2840,   0.0000,   9.8790,  34.9789,  62.0461,   0.0000,\n",
+      "          0.0000,  90.4281, 151.8807,   0.0000,   0.0000,  75.6426, 137.9153,\n",
+      "          0.0000,  65.4237], device='cuda:0')\n",
+      "tensor([1.1853e+02, 0.0000e+00, 0.0000e+00, 3.5182e+01, 8.2466e+01, 0.0000e+00,\n",
+      "        0.0000e+00, 1.7089e+01, 0.0000e+00, 2.8777e-02, 0.0000e+00, 0.0000e+00,\n",
+      "        6.7766e+01, 3.9360e+01, 1.2019e+02, 1.0623e+02], device='cuda:0')\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numba\n",
+    "from numba import cuda\n",
+    "\n",
+    "@cuda.jit\n",
+    "def batch_barrier_option(d_s, T, K, B, S0, sigma, mu, r, d_normals, N_STEPS, N_PATHS, N_BATCH):\n",
+    "    # ii - overall thread index\n",
+    "    ii = cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x\n",
+    "    stride = cuda.gridDim.x * cuda.blockDim.x\n",
+    "    tmp3 = math.sqrt(T/N_STEPS)\n",
+    "    for i in range(ii, N_PATHS * N_BATCH, stride):\n",
+    "        batch_id = i // N_PATHS\n",
+    "        path_id = i % N_PATHS\n",
+    "        tmp1 = mu[batch_id]*T/N_STEPS\n",
+    "        tmp2 = math.exp(-r[batch_id]*T)\n",
+    "        running_average = 0.0\n",
+    "        s_curr = S0[batch_id]\n",
+    "        for n in range(N_STEPS):\n",
+    "\n",
+    "            s_curr += tmp1 * s_curr + sigma[batch_id]*s_curr*tmp3*d_normals[path_id + batch_id * N_PATHS + n * N_PATHS * N_BATCH]\n",
+    "            running_average = running_average + 1.0/(n + 1.0) * (s_curr - running_average)\n",
+    "            if i==0 and batch_id == 2:\n",
+    "                print(s_curr)\n",
+    "            if running_average <= B[batch_id]:\n",
+    "                break\n",
+    "        payoff = running_average - K[batch_id] if running_average > K[batch_id] else 0\n",
+    "        d_s[i] = tmp2 * payoff\n",
+    "\n",
+    "class NumbaOptionDataSet(object):\n",
+    "    \n",
+    "    def __init__(self, max_len=10, number_path = 1000, batch=2, threads=512, seed=15):\n",
+    "        self.num = 0\n",
+    "        self.max_length = max_len\n",
+    "        self.N_PATHS = number_path\n",
+    "        self.N_STEPS = 365\n",
+    "        self.N_BATCH = batch\n",
+    "        self.T = np.float32(1.0)\n",
+    "        self.output = cupy.zeros(self.N_BATCH*self.N_PATHS, dtype=cupy.float32) \n",
+    "        self.number_of_blocks = (self.N_PATHS * self.N_BATCH - 1) // threads + 1\n",
+    "        self.number_of_threads = threads\n",
+    "        cupy.random.seed(seed)\n",
+    "        \n",
+    "    def __len__(self):\n",
+    "        return self.max_length\n",
+    "        \n",
+    "    def __iter__(self):\n",
+    "        self.num = 0\n",
+    "        return self\n",
+    "    \n",
+    "    def __next__(self):\n",
+    "        if self.num > self.max_length:\n",
+    "            raise StopIteration\n",
+    "        X = cupy.random.rand(self.N_BATCH, 6, dtype=cupy.float32)\n",
+    "        # scale the [0, 1) random numbers to the correct range for each of the option parameters\n",
+    "        X = X * cupy.array([200.0, 0.99, 200.0, 0.4, 0.2, 0.2], dtype=cupy.float32)\n",
+    "        # make sure the Barrier is smaller than the Strike price\n",
+    "        X[:, 1] = X[:, 0] * X[:, 1]\n",
+    "        randoms = cupy.random.normal(0, 1, self.N_BATCH * self.N_PATHS * self.N_STEPS, dtype=cupy.float32)\n",
+    "        batch_barrier_option[(self.number_of_blocks,), (self.number_of_threads,)](self.output, self.T, X[:, 0], \n",
+    "                              X[:, 1], X[:, 2], X[:, 3], X[:, 4], X[:, 5], randoms, self.N_STEPS, self.N_PATHS, self.N_BATCH)\n",
+    "        o = self.output.reshape(self.N_BATCH, self.N_PATHS)\n",
+    "        Y = o.mean(axis = 1) \n",
+    "        self.num += 1\n",
+    "        return (from_dlpack(X.toDlpack()), from_dlpack(Y.toDlpack()))\n",
+    "ds = NumbaOptionDataSet(10, number_path=100000, batch=16, seed=15)\n",
+    "for i in ds:\n",
+    "    print(i[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model\n",
+    "To map the option parameters to price, we use 6 layers of fully connected neural network with hidden dimension 512 as inspired by [this paper](https://arxiv.org/abs/1809.02233). Writing this DL price model into a file `model.py`:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting model.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile model.py\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torch\n",
+    "\n",
+    "\n",
+    "class Net(nn.Module):\n",
+    "\n",
+    "    def __init__(self, hidden=1024):\n",
+    "        super(Net, self).__init__()\n",
+    "        self.fc1 = nn.Linear(6, hidden)\n",
+    "        self.fc2 = nn.Linear(hidden, hidden)\n",
+    "        self.fc3 = nn.Linear(hidden, hidden)\n",
+    "        self.fc4 = nn.Linear(hidden, hidden)\n",
+    "        self.fc5 = nn.Linear(hidden, hidden)\n",
+    "        self.fc6 = nn.Linear(hidden, 1)\n",
+    "        self.register_buffer('norm',\n",
+    "                             torch.tensor([200.0,\n",
+    "                                           198.0,\n",
+    "                                           200.0,\n",
+    "                                           0.4,\n",
+    "                                           0.2,\n",
+    "                                           0.2]))\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # normalize the parameter to range [0-1] \n",
+    "        x = x / self.norm\n",
+    "        x = F.elu(self.fc1(x))\n",
+    "        x = F.elu(self.fc2(x))\n",
+    "        x = F.elu(self.fc3(x))\n",
+    "        x = F.elu(self.fc4(x))\n",
+    "        x = F.elu(self.fc5(x))\n",
+    "        return self.fc6(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we know the random parameters' scaling factors, the input parameters are first scaled back to a range of (0-1) by dividing them by (200.0, 198.0, 200.0, 0.4, 0.2, 0.2). Then they are projected 5 times to the hidden dimension of 512 after the `ELu` activation function. `ELu` is chosen because we need to compute the second order differentiation of the parameters. If use ReLu, the second order differentiation will always be zero. The last layer is a linear layer that maps the hidden dimension to the predicted option price. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For training, we use [Ignite](https://github.com/pytorch/ignite) which is a high-level library to train neural networks in PyTorch. We use `MSELoss` as the loss function, `Adam` as the optimizer and `CosineAnnealingScheduler` as the learning rate scheduler. The following code is feeding the random option data to the pricing model to train it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ignite.engine import Engine, Events\n",
+    "from ignite.handlers import Timer\n",
+    "from torch.nn import MSELoss\n",
+    "from torch.optim import Adam\n",
+    "from ignite.contrib.handlers.param_scheduler import CosineAnnealingScheduler\n",
+    "from ignite.handlers import ModelCheckpoint\n",
+    "from model import Net\n",
+    "from cupy_dataset import OptionDataSet\n",
+    "timer = Timer(average=True)\n",
+    "model = Net().cuda()\n",
+    "loss_fn = MSELoss()\n",
+    "optimizer = Adam(model.parameters(), lr=1e-3)\n",
+    "dataset = OptionDataSet(max_len=10000, number_path = 1024, batch=4800)\n",
+    "\n",
+    "def train_update(engine, batch):\n",
+    "    model.train()\n",
+    "    optimizer.zero_grad()\n",
+    "    x = batch[0]\n",
+    "    y = batch[1]\n",
+    "    y_pred = model(x)\n",
+    "    loss = loss_fn(y_pred[:,0], y)\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "    return loss.item()\n",
+    "\n",
+    "trainer = Engine(train_update)\n",
+    "log_interval = 100\n",
+    "\n",
+    "scheduler = CosineAnnealingScheduler(optimizer, 'lr', 1e-4, 1e-6, len(dataset))\n",
+    "trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)\n",
+    "timer.attach(trainer,\n",
+    "             start=Events.EPOCH_STARTED,\n",
+    "             resume=Events.ITERATION_STARTED,\n",
+    "             pause=Events.ITERATION_COMPLETED,\n",
+    "             step=Events.ITERATION_COMPLETED)    \n",
+    "@trainer.on(Events.ITERATION_COMPLETED)\n",
+    "def log_training_loss(engine):\n",
+    "    iter = (engine.state.iteration - 1) % len(dataset) + 1\n",
+    "    if iter % log_interval == 0:\n",
+    "        print('loss', engine.state.output, 'average time', timer.value())\n",
+    "        \n",
+    "trainer.run(dataset, max_epochs=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The loss is keeping decreasing which means the pricing model can predict the option prices better. It takes about $12ms$ to compute one mini-batch in average, In the following sections, we will try to expore the full potentials of the GPU to accelerate the training."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### TensorCore mixed precision training\n",
+    "\n",
+    "The V100 GPUs have 640 tensor cores that can accelerate half precision matrix multiplication calculation which is the core computation done by the DL model. [Apex library](https://github.com/NVIDIA/apex) developed by NVIDIA makes mixed precision and distributed training in Pytorch easy. By changing 3 lines of code, it can use the tensor cores to accelerate the training. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from apex import amp\n",
+    "from ignite.engine import Engine, Events\n",
+    "from torch.nn import MSELoss\n",
+    "from ignite.handlers import Timer\n",
+    "from torch.optim import Adam\n",
+    "from ignite.contrib.handlers.param_scheduler import CosineAnnealingScheduler\n",
+    "from ignite.handlers import ModelCheckpoint\n",
+    "from model import Net\n",
+    "from cupy_dataset import OptionDataSet\n",
+    "timer = Timer(average=True)\n",
+    "model = Net().cuda()\n",
+    "loss_fn = MSELoss()\n",
+    "optimizer = Adam(model.parameters(), lr=1e-3)\n",
+    "# set the AMP optimization level to O1\n",
+    "opt_level = 'O1'\n",
+    "# wrap the optimizer and model\n",
+    "model, optimizer = amp.initialize(model, optimizer, opt_level=opt_level)\n",
+    "dataset = OptionDataSet(max_len=10000, number_path = 1024, batch=4800)\n",
+    "\n",
+    "def train_update(engine, batch):\n",
+    "    model.train()\n",
+    "    optimizer.zero_grad()\n",
+    "    x = batch[0]\n",
+    "    y = batch[1]\n",
+    "    y_pred = model(x)\n",
+    "    loss = loss_fn(y_pred[:,0], y)\n",
+    "    # amp handles the auto loss scaling\n",
+    "    with amp.scale_loss(loss, optimizer) as scaled_loss:\n",
+    "        scaled_loss.backward()\n",
+    "    optimizer.step()\n",
+    "    return loss.item()\n",
+    "\n",
+    "trainer = Engine(train_update)\n",
+    "log_interval = 100\n",
+    "timer.attach(trainer,\n",
+    "             start=Events.EPOCH_STARTED,\n",
+    "             resume=Events.ITERATION_STARTED,\n",
+    "             pause=Events.ITERATION_COMPLETED,\n",
+    "             step=Events.ITERATION_COMPLETED)    \n",
+    "scheduler = CosineAnnealingScheduler(optimizer, 'lr', 1e-4, 1e-6, len(dataset))\n",
+    "trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)\n",
+    "    \n",
+    "@trainer.on(Events.ITERATION_COMPLETED)\n",
+    "def log_training_loss(engine):\n",
+    "    iter = (engine.state.iteration - 1) % len(dataset) + 1\n",
+    "    if iter % log_interval == 0:\n",
+    "        print('loss', engine.state.output, 'average time', timer.value())\n",
+    "        \n",
+    "trainer.run(dataset, max_epochs=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It improves to compute each mini-batch in $8ms$. As we reduce the model weights to half precision for better performance, the loss need to be scaled to make sure the half precision dynamic range aligns with the computation. It is guessing what is the correct loss scaling factor and adjust it automatically if the gradient overflows. In the end, we will get the best hardware acceleration while maintaining the accuracy of model prediction."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multiple GPU training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Apex makes multiple GPU training easy. Working on the same training script, we need to take care of a few extra steps:\n",
+    "\n",
+    "1. Add the argument `--local_rank` which will be automatically set by the distributed launcher\n",
+    "2. Initialize the process group\n",
+    "2. Generate independent batched data based on process id in the dataset.\n",
+    "3. Wrap the model and optimizer to handle distributed computation. \n",
+    "4. Scale the loss and optimizer\n",
+    "\n",
+    "To launch distributed training, we need to put everything into a python file. Following is an example:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting distributed_train.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile distributed_train.py \n",
+    "import cupy\n",
+    "import numpy as np\n",
+    "import math\n",
+    "import time\n",
+    "import os\n",
+    "import torch\n",
+    "from torch.utils.dlpack import from_dlpack\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torch\n",
+    "from apex import amp\n",
+    "from ignite.engine import Engine, Events\n",
+    "from torch.nn import MSELoss\n",
+    "from torch.optim import Adam\n",
+    "from ignite.contrib.handlers.param_scheduler import CosineAnnealingScheduler\n",
+    "from ignite.handlers import ModelCheckpoint\n",
+    "from apex.parallel import DistributedDataParallel \n",
+    "import argparse\n",
+    "from model import Net\n",
+    "from cupy_dataset import OptionDataSet\n",
+    "\n",
+    "parser = argparse.ArgumentParser()\n",
+    "parser = argparse.ArgumentParser()\n",
+    "# this local_rank arg is automaticall set by distributed launch\n",
+    "parser.add_argument(\"--local_rank\", default=0, type=int)\n",
+    "args = parser.parse_args()\n",
+    "\n",
+    "args.distributed = False\n",
+    "if 'WORLD_SIZE' in os.environ:\n",
+    "    args.distributed = int(os.environ['WORLD_SIZE']) > 1\n",
+    "\n",
+    "if args.distributed:\n",
+    "    torch.cuda.set_device(args.local_rank)\n",
+    "    torch.distributed.init_process_group(backend='nccl',\n",
+    "                                         init_method='env://')\n",
+    "\n",
+    "torch.backends.cudnn.benchmark = True\n",
+    "\n",
+    "\n",
+    "model = Net().cuda()\n",
+    "loss_fn = MSELoss()\n",
+    "optimizer = Adam(model.parameters(), lr=1e-3)\n",
+    "opt_level = 'O1'\n",
+    "model, optimizer = amp.initialize(model, optimizer, opt_level=opt_level)\n",
+    "if args.distributed:\n",
+    "    model = DistributedDataParallel(model)\n",
+    "dataset = OptionDataSet(max_len=10000, number_path = 1024, batch=10240, seed=args.local_rank)\n",
+    "\n",
+    "def train_update(engine, batch):\n",
+    "    model.train()\n",
+    "    optimizer.zero_grad()\n",
+    "    x = batch[0]\n",
+    "    y = batch[1]\n",
+    "    y_pred = model(x)\n",
+    "    loss = loss_fn(y_pred[:,0], y)\n",
+    "    with amp.scale_loss(loss, optimizer) as scaled_loss:\n",
+    "        scaled_loss.backward()\n",
+    "    optimizer.step()\n",
+    "    return loss.item()\n",
+    "\n",
+    "trainer = Engine(train_update)\n",
+    "log_interval = 100\n",
+    "\n",
+    "scheduler = CosineAnnealingScheduler(optimizer, 'lr', 1e-4, 1e-6, len(dataset))\n",
+    "trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)\n",
+    "    \n",
+    "@trainer.on(Events.ITERATION_COMPLETED)\n",
+    "def log_training_loss(engine):\n",
+    "    iter = (engine.state.iteration - 1) % len(dataset) + 1\n",
+    "    if iter % log_interval == 0:\n",
+    "        print('loss', engine.state.output)\n",
+    "        \n",
+    "trainer.run(dataset, max_epochs=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To launch multiple processes training, we need to run the following command:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%reset -f\n",
+    "\n",
+    "!python -m torch.distributed.launch --nproc_per_node=4 distributed_train.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It works and all the GPUs are busy to train this network. However, it has a few problems:-\n",
+    "   \n",
+    "    1. There is no model serialization so the trained model is not saved\n",
+    "    2. There is no validation dataset to check the training progress\n",
+    "    3. Most of the time is spent in Monte Carlo simulation hence the training is slow\n",
+    "    4. We use a few paths(1024) for each option parameter set which is noise and the model cannot converge to a low cost value.\n",
+    "We will address these problems in the next notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/asian_barrier_option/deep_learning_option_2.ipynb b/notebooks/asian_barrier_option/deep_learning_option_2.ipynb
new file mode 100644
index 00000000..fee3afcf
--- /dev/null
+++ b/notebooks/asian_barrier_option/deep_learning_option_2.ipynb
@@ -0,0 +1,1100 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deep Learning Model for Asian Barrier Options\n",
+    "\n",
+    "As shown in the previous notebook, there are a few problems to generate data on the fly \n",
+    "   \n",
+    "    1. There is no model serialization so the trained model is not saved\n",
+    "    2. There is no validation dataset to check the training progress\n",
+    "    3. Most of the time is spent on Monte Carlo simulation hence the training is slow\n",
+    "    4. We use a few paths(1024) for each option parameter set which is noise and the model cannot converge to a low cost value.\n",
+    "The solution is to save the Monte Carlo simulation data on the disk. This allows us to\n",
+    "\n",
+    "    1. Reuse the same dataset for different models and save the Monte Carlo simulation time\n",
+    "    2. Generate more accurate pricing data by increasing the number of paths\n",
+    "    \n",
+    "We will use CuPy to run the Monte Carlo simulation as it is the most efficient way. Taking the same OptionDataSet defined in the previous notebook:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cupy_dataset import OptionDataSet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Making the directories for the saved data files and the model check points:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mkdir -p datafiles\n",
+    "!mkdir -p check_points"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Defining a function to generate the dataset file:- "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "def gen_data(n_files = 630, options_per_file = 10000, seed=3):\n",
+    "    counter = 0\n",
+    "    ds = OptionDataSet(max_len=n_files * options_per_file, number_path=8192000, batch=1,\n",
+    "                   seed=seed)\n",
+    "    x = []\n",
+    "    y = []\n",
+    "    for i in ds:\n",
+    "        if counter!=0 and counter % options_per_file == 0:\n",
+    "            filename = 'datafiles/'+str(seed) + '_' + str(counter//options_per_file) + '.pth'\n",
+    "            state = (torch.cat(x, 0), torch.cat(y, 0))\n",
+    "            torch.save(state, filename)\n",
+    "            x = []\n",
+    "            y = []\n",
+    "        x.append(i[0].cpu())\n",
+    "        y.append(i[1].cpu())\n",
+    "        counter += 1\n",
+    "    return seed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It will generate files that contain `X` and `Y` matrix of size `option_per_file` and the filenames are in the format of `seed_group.pth`, we can test run with `n_files` = 5 and `options_per_file` = 16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[1.7910e+02, 6.8079e+01, 1.0688e+02, 2.5889e-01, 1.7393e-01, 1.4359e-01],\n",
+      "        [1.3597e+02, 5.8014e+01, 1.0772e+02, 1.1119e-01, 1.1278e-01, 3.3107e-03],\n",
+      "        [4.7951e+01, 3.6957e+01, 8.0480e+01, 2.6536e-01, 5.3653e-02, 7.2782e-02],\n",
+      "        [1.0026e+02, 8.1533e+00, 6.6216e+01, 3.8491e-02, 5.5396e-02, 1.4566e-01],\n",
+      "        [1.0416e+02, 7.9586e+01, 1.0620e+02, 1.2557e-01, 1.9639e-02, 3.0966e-02],\n",
+      "        [1.6851e+02, 9.7813e+01, 1.2468e+02, 1.1845e-01, 7.9473e-02, 1.0369e-01],\n",
+      "        [1.6673e+02, 7.4595e+01, 6.4872e+01, 3.8445e-01, 4.0116e-02, 1.5097e-01],\n",
+      "        [3.2400e+01, 1.4736e+01, 9.4934e+01, 2.5872e-01, 6.7174e-02, 1.0737e-01],\n",
+      "        [1.2953e+02, 8.5337e+01, 1.2570e+02, 1.6452e-01, 7.1083e-02, 1.9993e-01],\n",
+      "        [1.5920e+02, 1.3722e+02, 6.4502e+01, 3.5891e-01, 1.5036e-01, 1.8909e-01],\n",
+      "        [4.7439e+00, 6.8898e-01, 1.7892e+01, 1.6206e-02, 1.1772e-01, 1.1536e-01],\n",
+      "        [1.4590e+02, 5.5645e+00, 9.4114e+00, 9.8751e-02, 7.2455e-03, 1.2266e-01],\n",
+      "        [1.0537e+02, 4.6149e+01, 7.2182e+01, 2.0814e-01, 1.5636e-02, 4.7667e-02],\n",
+      "        [1.9498e+02, 1.4687e+02, 5.9092e+01, 5.9770e-02, 4.7395e-02, 8.9560e-02],\n",
+      "        [5.4070e+00, 4.4146e+00, 1.3971e+02, 3.4593e-01, 1.8324e-01, 1.3890e-01],\n",
+      "        [6.1022e+01, 3.5528e+01, 3.8339e+01, 1.4686e-01, 1.2386e-01, 1.2188e-01]])\n",
+      "tensor([2.1621e-02, 1.0037e-02, 3.2299e+01, 0.0000e+00, 4.7080e+00, 2.7595e-04,\n",
+      "        0.0000e+00, 5.9109e+01, 4.3838e+00, 0.0000e+00, 1.2694e+01, 0.0000e+00,\n",
+      "        4.3242e-03, 0.0000e+00, 1.2877e+02, 2.6165e-06])\n"
+     ]
+    }
+   ],
+   "source": [
+    "gen_data(n_files=5, options_per_file = 16, seed=3)\n",
+    "X, Y = torch.load('datafiles/3_1.pth')\n",
+    "print(X)\n",
+    "print(Y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use DASK to generate dataset on multipe GPUs in this notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Client</h3>\n",
+       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:43453</li>\n",
+       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
+       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Workers: </b>8</li>\n",
+       "  <li><b>Cores: </b>8</li>\n",
+       "  <li><b>Memory: </b>540.94 GB</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: 'tcp://127.0.0.1:43453' processes=8 threads=8, memory=540.94 GB>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import dask\n",
+    "import dask_cudf\n",
+    "from dask.delayed import delayed\n",
+    "from dask_cuda import LocalCUDACluster\n",
+    "cluster = LocalCUDACluster()\n",
+    "from dask.distributed import Client\n",
+    "client = Client(cluster)\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Following code is an example that generates `100x5x16` data points on 4 GPUs. For serious Deep Learning model training, we need millions of data points. You can try to change `n_files` and `options_per_file` to larger numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0,\n",
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+       " 98,\n",
+       " 99]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "futures = []\n",
+    "for i in range(0, 100):\n",
+    "    future = client.submit(gen_data, 5, 16, i)\n",
+    "    futures.append(future)\n",
+    "results = client.gather(futures)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once millions of data points are generated, we can combine the data points together and split them into training and validation datasets. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    }
+   ],
+   "source": [
+    "import pathlib\n",
+    "\n",
+    "files = list(pathlib.Path('datafiles/').glob('*.pth'))\n",
+    "trn_size = int(len(files)*0.7)\n",
+    "trn_files = files[:trn_size]\n",
+    "val_files = files[trn_size:]\n",
+    "\n",
+    "trn_x = []\n",
+    "trn_y = []\n",
+    "count = 0\n",
+    "\n",
+    "for i in trn_files:\n",
+    "    tensor = torch.load(i)\n",
+    "    if count % 10 == 0:\n",
+    "        print(count,'/',len(trn_files))\n",
+    "    trn_x.append(tensor[0])\n",
+    "    trn_y.append(tensor[1])\n",
+    "    count += 1\n",
+    "\n",
+    "X = torch.cat(trn_x)\n",
+    "Y = torch.cat(trn_y)\n",
+    "torch.save((X,Y), 'trn.pth')\n",
+    "\n",
+    "val_x = []\n",
+    "val_y = []\n",
+    "count = 0\n",
+    "\n",
+    "for i in val_files:\n",
+    "    tensor = torch.load(i)\n",
+    "    if count % 10 == 0:\n",
+    "        print(count,'/',len(val_files))\n",
+    "    val_x.append(tensor[0])\n",
+    "    val_y.append(tensor[1])\n",
+    "    count += 1\n",
+    "\n",
+    "X = torch.cat(val_x)\n",
+    "Y = torch.cat(val_y)\n",
+    "torch.save((X,Y), 'val.pth')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We created two data files `trn.pth` and `val.pth` for training and validation. We can define a new PyTorch Dataset to load data from file and write it to file. This dataset takes rank and world_size arguments for distributed training. It loads the whole dataset into the GPU memory and samples the data points according to the rank id so that dataset of different rank_id gives different data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing filedataset.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile filedataset.py\n",
+    "import torch\n",
+    "\n",
+    "\n",
+    "class OptionDataSet(torch.utils.data.Dataset):\n",
+    "    def __init__(self, filename, rank=0, world_size=5):\n",
+    "        tensor = torch.load(filename)\n",
+    "        self.tensor = (tensor[0].cuda(), tensor[1].cuda())\n",
+    "        self.length = len(self.tensor[0]) // world_size\n",
+    "        self.world_size = world_size\n",
+    "        self.rank = rank\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        index = index * self.world_size + self.rank\n",
+    "        return self.tensor[0][index], self.tensor[1][index]\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return self.length"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When training the deep learning models, one effective way to prevent over-fitting is to have separate validation dataset to monitor the out of sample performance. When the validation dataset  performance declines,  it means over-fitting is happening so we can  stop the training. We put everything together into one script that can train the model efficiently in multiple GPUs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting distributed_training.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile distributed_training.py\n",
+    "import torch\n",
+    "from ignite.engine import Engine, Events\n",
+    "from torch.nn import MSELoss\n",
+    "from ignite.contrib.handlers.param_scheduler import CosineAnnealingScheduler\n",
+    "from apex import amp\n",
+    "import argparse\n",
+    "import os\n",
+    "from apex.parallel import DistributedDataParallel\n",
+    "import apex\n",
+    "from apex.optimizers import FusedLAMB\n",
+    "from model import Net\n",
+    "from filedataset import OptionDataSet\n",
+    "from ignite.metrics import MeanAbsoluteError\n",
+    "import ignite\n",
+    "import shutil\n",
+    "import torch.distributed as dist\n",
+    "\n",
+    "parser = argparse.ArgumentParser()\n",
+    "parser.add_argument(\"--local_rank\", default=0, type=int)\n",
+    "parser.add_argument(\"--path\", default=None)\n",
+    "parser.add_argument(\"--mae_improv_tol\", default=0.002, type=float)\n",
+    "args = parser.parse_args()\n",
+    "\n",
+    "args.distributed = False\n",
+    "if 'WORLD_SIZE' in os.environ:\n",
+    "    args.distributed = int(os.environ['WORLD_SIZE']) > 1\n",
+    "\n",
+    "if args.distributed:\n",
+    "    torch.cuda.set_device(args.local_rank)\n",
+    "    torch.distributed.init_process_group(backend='nccl',\n",
+    "                                         init_method='env://')\n",
+    "\n",
+    "torch.backends.cudnn.benchmark = True\n",
+    "\n",
+    "trn_dataset = OptionDataSet(filename='./trn.pth',\n",
+    "                            rank=dist.get_rank(),\n",
+    "                            world_size=int(os.environ['WORLD_SIZE']))\n",
+    "trn_dataset = torch.utils.data.DataLoader(trn_dataset,\n",
+    "                                          batch_size=1024,\n",
+    "                                          shuffle=True,\n",
+    "                                          num_workers=0)\n",
+    "\n",
+    "val_dataset = OptionDataSet(filename='./val.pth',\n",
+    "                            rank=dist.get_rank(),\n",
+    "                            world_size=int(os.environ['WORLD_SIZE']))\n",
+    "val_dataset = torch.utils.data.DataLoader(val_dataset,\n",
+    "                                          batch_size=1024,\n",
+    "                                          shuffle=False,\n",
+    "                                          num_workers=0)\n",
+    "\n",
+    "model = Net().cuda()\n",
+    "optimizer = FusedLAMB(model.parameters(), lr=1e-3)\n",
+    "loss_fn = MSELoss()\n",
+    "\n",
+    "\n",
+    "model = apex.parallel.convert_syncbn_model(model, channel_last=True)\n",
+    "model, optimizer = amp.initialize(model, optimizer, opt_level='O1')\n",
+    "\n",
+    "\n",
+    "best_mae = 100000\n",
+    "\n",
+    "if args.path is not None:\n",
+    "    def resume():\n",
+    "        global best_mae\n",
+    "        checkpoint = torch.load(args.path)\n",
+    "        best_mae = checkpoint['best_mae']\n",
+    "        model.load_state_dict(checkpoint['state_dict'])\n",
+    "        amp.load_state_dict(checkpoint['amp'])\n",
+    "        optimizer.load_state_dict(checkpoint['optimizer'])\n",
+    "    resume()\n",
+    "\n",
+    "\n",
+    "if args.distributed:\n",
+    "    model = DistributedDataParallel(model)\n",
+    "    \n",
+    "\n",
+    "def train_update(engine, batch):\n",
+    "    model.train()\n",
+    "    optimizer.zero_grad()\n",
+    "    x = batch[0]\n",
+    "    y = batch[1]\n",
+    "    y_pred = model(x)\n",
+    "    loss = loss_fn(y, y_pred[:, 0])\n",
+    "    with amp.scale_loss(loss, optimizer) as scaled_loss:\n",
+    "        scaled_loss.backward()\n",
+    "    optimizer.step()\n",
+    "    return loss.item()\n",
+    "\n",
+    "trainer = Engine(train_update)\n",
+    "log_interval = 500\n",
+    "\n",
+    "scheduler = CosineAnnealingScheduler(optimizer, 'lr', 1e-5, 5e-6,\n",
+    "                                     len(trn_dataset),\n",
+    "                                     start_value_mult=0.999, end_value_mult=0.999,\n",
+    "                                     save_history=False\n",
+    "                                     )\n",
+    "trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)\n",
+    "\n",
+    "\n",
+    "def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):\n",
+    "    torch.save(state, filename)\n",
+    "    if is_best:\n",
+    "        shutil.copyfile(filename, 'check_points/model_best.pth.tar')\n",
+    "\n",
+    "\n",
+    "@trainer.on(Events.ITERATION_COMPLETED)\n",
+    "def log_training_loss(engine):\n",
+    "    iter = (engine.state.iteration - 1) % len(trn_dataset) + 1\n",
+    "    if iter % log_interval == 0:\n",
+    "        print('loss', engine.state.output, 'iter', engine.state.iteration,\n",
+    "              'lr', scheduler.get_param())\n",
+    "\n",
+    "\n",
+    "metric = MeanAbsoluteError()\n",
+    "loss_m = ignite.metrics.Loss(loss_fn)\n",
+    "\n",
+    "# run eval at one process only\n",
+    "def eval_update(engine, batch):\n",
+    "    model.eval()\n",
+    "    x = batch[0]\n",
+    "    y = batch[1]\n",
+    "    y_pred = model(x)\n",
+    "    return y, y_pred[:, 0]\n",
+    "evaluator = Engine(eval_update)\n",
+    "metric.attach(evaluator, \"MAE\")\n",
+    "loss_m.attach(evaluator, \"loss\")\n",
+    "        \n",
+    "@trainer.on(Events.EPOCH_COMPLETED)\n",
+    "def log_evalnumber(engine):\n",
+    "    global best_mae\n",
+    "    mae_improv_tol = args.mae_improv_tol  # default 0.002 or 0.2% improvement\n",
+    "    evaluator.run(val_dataset, max_epochs=1)\n",
+    "    metrics = evaluator.state.metrics\n",
+    "    average_tensor = torch.tensor([metrics['MAE'], metrics['loss']]).cuda()\n",
+    "    torch.distributed.reduce(average_tensor, 0, op=torch.distributed.ReduceOp.SUM)\n",
+    "    torch.distributed.broadcast(average_tensor, 0)\n",
+    "    average_tensor = average_tensor/int(os.environ['WORLD_SIZE'])\n",
+    "\n",
+    "    mae = average_tensor[0].item()\n",
+    "    is_best = False\n",
+    "    if (1 - mae / best_mae) >= mae_improv_tol or \\\n",
+    "            (engine.state.epoch == engine.state.max_epochs and\n",
+    "             mae < best_mae):\n",
+    "        best_mae = mae\n",
+    "        is_best = True\n",
+    "\n",
+    "    # print(\"RANK {}   Val Results - Epoch: {}  Avg MAE: {:.5f} loss: {:.5f} BEST MAE: {:.5f}\"\n",
+    "    #      .format(dist.get_rank(), trainer.state.epoch, metrics['MAE'], metrics['loss'], best_mae))\n",
+    "\n",
+    "    if dist.get_rank() == 0:\n",
+    "        print('Epoch {}/{}'.format(engine.state.epoch, engine.state.max_epochs))\n",
+    "        print('Best MAE Improvement Tolerance for checkpointing: {}%'.format(100 * mae_improv_tol))\n",
+    "        print(\"RANK {} AVG {} NGPUs, best-mae: {:.5f} mae: {:.5f} loss: {:.5f}\".format(\n",
+    "            dist.get_rank(),\n",
+    "            int(os.environ['WORLD_SIZE']),\n",
+    "            best_mae,\n",
+    "            average_tensor[0].item(),\n",
+    "            average_tensor[1].item()))\n",
+    "        fname = 'check_points/current_pth.tar'\n",
+    "        if is_best:\n",
+    "            save_checkpoint({'epoch': trainer.state.epoch,\n",
+    "                             'state_dict': model.module.state_dict(),\n",
+    "                             'best_mae': best_mae,\n",
+    "                             'optimizer': optimizer.state_dict(),\n",
+    "                             'amp': amp.state_dict()\n",
+    "                             }, is_best,\n",
+    "                            filename=fname)\n",
+    "        inputs = torch.tensor([[110.0, 100.0, 120.0, 0.35, 0.1, 0.05]]).cuda()\n",
+    "        res = model(inputs)\n",
+    "        print('test one example:', res.item())\n",
+    "\n",
+    "trainer.run(trn_dataset, max_epochs=2000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compared to the last notebook, it is a little complicated because \n",
+    "* it handles the validation dataset evaluation\n",
+    "* it serializes the model into a file and keeps track of the best performed model based on the mean absolute error(MAE)\n",
+    "* it resumes the training from the file\n",
+    "\n",
+    "We can launch the distributed training by the following command:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ngpus=!echo $(nvidia-smi -L | wc -l)\n",
+    "!python -m torch.distributed.launch --nproc_per_node={ngpus[0]} distributed_training.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need some patience to train the pricing model until it converges."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Inference and Greeks\n",
+    "Once the training is converged, the best performed model is saved into `check_points/` directory. \n",
+    "\n",
+    "To get a good model, you need millions of data points to train the model until it converges. Usually it takes 10-20 hours in a single 8 GPUs DGX-1 machine. We trained the model with 10 million training data points and 5 million validation data points. We didn't explore what is the minimum number of training samples but simply use large number of data samples. You may get away by using less data points for training. \n",
+    "\n",
+    "To save your time, you can run the following commands to download the weights and use them for the inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2020-03-02 19:50:47--  https://query.data.world/s/fb3ilrt77qcpx7kwnfgr3cybvdctk2\n",
+      "Resolving query.data.world (query.data.world)... 3.222.149.11, 54.86.110.27, 54.85.70.45\n",
+      "Connecting to query.data.world (query.data.world)|3.222.149.11|:443... connected.\n",
+      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
+      "Location: https://download.data.world/file_download/yidata/weights1024/model_best.pth.tar?auth=eyJhbGciOiJIUzUxMiJ9.eyJzdWIiOiJwcm9kLXVzZXItY2xpZW50OnlpZGF0YSIsImlzcyI6ImFnZW50OnlpZGF0YTo6ZjJkYTQ1NmUtYjA5MC00ZjdiLTgyNTYtOWU0ZTFjNTA5ZGRmIiwiaWF0IjoxNTgxNzAzMDY5LCJyb2xlIjpbInVzZXIiLCJ1c2VyX2FwaV9hZG1pbiIsInVzZXJfYXBpX3JlYWQiLCJ1c2VyX2FwaV93cml0ZSJdLCJnZW5lcmFsLXB1cnBvc2UiOmZhbHNlLCJ1cmwiOiJiZTQxOTdiNDQ2OTZjOWFjNmRjNmFlMDZjMzQxZGE5ZmI2MTY4ODZhIn0.ivgs7kPSxaf1EkRk_CfBrH8BNquYpkiFHnFDOAiY4_9MxfluMFREgtmUUftiYD7536Y6PsNC-x62FrtoZC4JXA [following]\n",
+      "--2020-03-02 19:50:47--  https://download.data.world/file_download/yidata/weights1024/model_best.pth.tar?auth=eyJhbGciOiJIUzUxMiJ9.eyJzdWIiOiJwcm9kLXVzZXItY2xpZW50OnlpZGF0YSIsImlzcyI6ImFnZW50OnlpZGF0YTo6ZjJkYTQ1NmUtYjA5MC00ZjdiLTgyNTYtOWU0ZTFjNTA5ZGRmIiwiaWF0IjoxNTgxNzAzMDY5LCJyb2xlIjpbInVzZXIiLCJ1c2VyX2FwaV9hZG1pbiIsInVzZXJfYXBpX3JlYWQiLCJ1c2VyX2FwaV93cml0ZSJdLCJnZW5lcmFsLXB1cnBvc2UiOmZhbHNlLCJ1cmwiOiJiZTQxOTdiNDQ2OTZjOWFjNmRjNmFlMDZjMzQxZGE5ZmI2MTY4ODZhIn0.ivgs7kPSxaf1EkRk_CfBrH8BNquYpkiFHnFDOAiY4_9MxfluMFREgtmUUftiYD7536Y6PsNC-x62FrtoZC4JXA\n",
+      "Resolving download.data.world (download.data.world)... 54.85.70.45, 54.86.110.27, 3.222.149.11\n",
+      "Connecting to download.data.world (download.data.world)|54.85.70.45|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: unspecified [application/x-tar]\n",
+      "Saving to: ‘./check_points/model_best.pth.tar’\n",
+      "\n",
+      "./check_points/mode     [         <=>        ]  48.15M  29.9MB/s    in 1.6s    \n",
+      "\n",
+      "2020-03-02 19:50:51 (29.9 MB/s) - ‘./check_points/model_best.pth.tar’ saved [50484852]\n",
+      "\n",
+      "--2020-03-02 19:50:51--  https://query.data.world/s/o2kzs74pg22mc2mfyhkykyu6pq36yr\n",
+      "Resolving query.data.world (query.data.world)... 3.222.149.11, 54.86.110.27, 54.85.70.45\n",
+      "Connecting to query.data.world (query.data.world)|3.222.149.11|:443... connected.\n",
+      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
+      "Location: https://download.data.world/file_download/yidata/weight512/model_best.pth.tar?auth=eyJhbGciOiJIUzUxMiJ9.eyJzdWIiOiJwcm9kLXVzZXItY2xpZW50OnlpZGF0YSIsImlzcyI6ImFnZW50OnlpZGF0YTo6ZjJkYTQ1NmUtYjA5MC00ZjdiLTgyNTYtOWU0ZTFjNTA5ZGRmIiwiaWF0IjoxNTgxNzAzMTU2LCJyb2xlIjpbInVzZXIiLCJ1c2VyX2FwaV9hZG1pbiIsInVzZXJfYXBpX3JlYWQiLCJ1c2VyX2FwaV93cml0ZSJdLCJnZW5lcmFsLXB1cnBvc2UiOmZhbHNlLCJ1cmwiOiIxYTMyMjY4YzA4YjMzYzJiMzlhMjg5MTA4NDE5OGFiZjNjZWExNzdmIn0.QEUxrUZ0uyXu2-cLU6JrhmNHWwScObX0NYghH8UdLP8SJXA6AefVZrtRBINeK6j_iM8ibOzJ19FidH1r5BsJbA [following]\n",
+      "--2020-03-02 19:50:51--  https://download.data.world/file_download/yidata/weight512/model_best.pth.tar?auth=eyJhbGciOiJIUzUxMiJ9.eyJzdWIiOiJwcm9kLXVzZXItY2xpZW50OnlpZGF0YSIsImlzcyI6ImFnZW50OnlpZGF0YTo6ZjJkYTQ1NmUtYjA5MC00ZjdiLTgyNTYtOWU0ZTFjNTA5ZGRmIiwiaWF0IjoxNTgxNzAzMTU2LCJyb2xlIjpbInVzZXIiLCJ1c2VyX2FwaV9hZG1pbiIsInVzZXJfYXBpX3JlYWQiLCJ1c2VyX2FwaV93cml0ZSJdLCJnZW5lcmFsLXB1cnBvc2UiOmZhbHNlLCJ1cmwiOiIxYTMyMjY4YzA4YjMzYzJiMzlhMjg5MTA4NDE5OGFiZjNjZWExNzdmIn0.QEUxrUZ0uyXu2-cLU6JrhmNHWwScObX0NYghH8UdLP8SJXA6AefVZrtRBINeK6j_iM8ibOzJ19FidH1r5BsJbA\n",
+      "Resolving download.data.world (download.data.world)... 3.222.149.11, 54.86.110.27, 54.85.70.45\n",
+      "Connecting to download.data.world (download.data.world)|3.222.149.11|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: unspecified [application/x-tar]\n",
+      "Saving to: ‘./check_points/512/model_best.pth.tar’\n",
+      "\n",
+      "./check_points/512/     [     <=>            ]  12.08M  13.8MB/s    in 0.9s    \n",
+      "\n",
+      "2020-03-02 19:50:52 (13.8 MB/s) - ‘./check_points/512/model_best.pth.tar’ saved [12662389]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "! ((test ! -f './check_points/model_best.pth.tar' ||  test ! -f './check_points/512/model_best.pth.tar') && \\\n",
+    "  bash ./download_data.sh) || echo \"Dataset is already present. No need to re-download it.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can load the model parameters and use it to do inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[18.7140]], device='cuda:0', grad_fn=<AddmmBackward>)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from model import Net\n",
+    "import torch\n",
+    "checkpoint = torch.load('check_points/model_best.pth.tar')\n",
+    "model = Net().cuda()\n",
+    "model.load_state_dict(checkpoint['state_dict'])\n",
+    "inputs = torch.tensor([[110.0, 100.0, 120.0, 0.35, 0.1, 0.05]]).cuda()\n",
+    "model(inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the benefits of building a deep learning model is that the [Greeks](<https://en.wikipedia.org/wiki/Greeks_(finance)#First-order_Greeks>) can be easily computed. We just need to take advantage of the auto-grad feature in Pytorch. Following shows an example of calculating the first order differentiation for parameters 'K, B, S0, sigma, mu, r'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-6.7092e-01, -2.1257e-02,  7.8896e-01,  1.9219e+01,  4.8331e+01,\n",
+       "         -1.8419e+01]], device='cuda:0')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs = torch.tensor([[110.0, 100.0, 120.0, 0.35, 0.1, 0.05]]).cuda()\n",
+    "inputs.requires_grad = True\n",
+    "x = model(inputs)\n",
+    "x.backward()\n",
+    "first_order_gradient = inputs.grad\n",
+    "first_order_gradient"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we are going to plot the Delta graph:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fa5e84fa9e8>]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import pylab\n",
+    "import numpy as np\n",
+    "def compute_delta(S):\n",
+    "    inputs = torch.tensor([[110.0, 100.0, S, 0.35, 0.1, 0.05]]).cuda()\n",
+    "    inputs.requires_grad = True\n",
+    "    x = model(inputs)\n",
+    "    x.backward()\n",
+    "    first_order_gradient = inputs.grad\n",
+    "    return first_order_gradient[0][2]\n",
+    "prices = np.arange(10, 200, 0.1)\n",
+    "deltas = []\n",
+    "for p in prices:\n",
+    "    deltas.append(compute_delta(p).item())\n",
+    "fig = pylab.plot(prices, deltas)\n",
+    "pylab.xlabel('prices')\n",
+    "pylab.ylabel('Delta')\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculating the second order derivative is easy in PyTorch too, following is an example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([[ 6.9817e-01, -3.2722e-01,  3.9008e-02, -3.5628e+01, -7.0561e+00,\n",
+       "          -5.2492e+01]], device='cuda:0'),)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from torch import Tensor\n",
+    "from torch.autograd import Variable\n",
+    "from torch.autograd import grad\n",
+    "from torch import nn\n",
+    "\n",
+    "inputs = torch.tensor([[110.0, 100.0, 120.0, 0.35, 0.1, 0.05]]).cuda()\n",
+    "inputs.requires_grad = True\n",
+    "x = model(inputs)\n",
+    "\n",
+    "# instead of using loss.backward(), use torch.autograd.grad() to compute gradients\n",
+    "# https://pytorch.org/docs/stable/autograd.html#torch.autograd.grad\n",
+    "loss_grads = grad(x, inputs, create_graph=True)\n",
+    "drv = grad(loss_grads[0], inputs, torch.ones_like(loss_grads[0]) )\n",
+    "drv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Gamma is the second order differenation of `S`. We can plot the the Gamma curve as a function of the stock price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fa5e84a69e8>]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pylab\n",
+    "import numpy as np\n",
+    "def compute_gamma(S):\n",
+    "    inputs = torch.tensor([[110.0, 100.0, S, 0.35, 0.1, 0.05]]).cuda()\n",
+    "    inputs.requires_grad = True\n",
+    "    x = model(inputs)\n",
+    "    loss_grads = grad(x, inputs, create_graph=True)\n",
+    "    drv = grad(loss_grads[0], inputs, torch.ones_like(loss_grads[0]) )\n",
+    "    return drv[0][0][2]\n",
+    "\n",
+    "prices = np.arange(10, 200, 0.1)\n",
+    "deltas = []\n",
+    "for p in prices:\n",
+    "    deltas.append(compute_gamma(p).item())\n",
+    "fig2 = pylab.plot(prices, deltas)\n",
+    "pylab.xlabel('prices')\n",
+    "pylab.ylabel('Gamma')\n",
+    "fig2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Implied volatility](https://en.wikipedia.org/wiki/Implied_volatility) is the forecasted volatility of the underlying asset based on the quoted prices of the option. It is the reverse mapping of price to the option parameter given the model which is hard to do with the Monte Carlo simulation approach. But if we have the deep learning pricing model, it is an easy task. We can first plot the relationship between volatility and the option price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fa5e841c2b0>]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pylab\n",
+    "import numpy as np\n",
+    "def compute_price(sigma):\n",
+    "    inputs = torch.tensor([[110.0, 100.0, 120.0, sigma, 0.1, 0.05]]).cuda()\n",
+    "    x = model(inputs)\n",
+    "    return x.item()\n",
+    "sigmas = np.arange(0, 0.5, 0.1)\n",
+    "prices = []\n",
+    "for s in sigmas:\n",
+    "    prices.append(compute_price(s))\n",
+    "fig3 = pylab.plot(sigmas, prices)\n",
+    "pylab.xlabel('Sigma')\n",
+    "pylab.ylabel('Price')\n",
+    "fig3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given the prices `P`, the implied volatility is the root of the function `compute_price`. We can use bisection to find the root."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "implied volativity 0.18517351150512695 error 4.76837158203125e-06\n"
+     ]
+    }
+   ],
+   "source": [
+    "def bisection_root(small, large, fun, target, EPS=1e-6):\n",
+    "    if fun(large) - target < 0:\n",
+    "        print('upper bound is too small')\n",
+    "        return None\n",
+    "    if fun(small) - target > 0:\n",
+    "        print('lower bound is too large')\n",
+    "        return None\n",
+    "    while large - small > EPS:\n",
+    "        mid = (large + small) / 2.0\n",
+    "        if fun(mid) - target >= 0:\n",
+    "            large = mid\n",
+    "        else:\n",
+    "            small = mid\n",
+    "    mid = (large + small) / 2.0\n",
+    "    return mid, abs(fun(mid) - target)\n",
+    "quoted_price = 16.0\n",
+    "sigma, err = bisection_root(0, 0.5, compute_price, quoted_price)\n",
+    "print('implied volativity', sigma, 'error', err)     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/asian_barrier_option/docker/Dockerfile b/notebooks/asian_barrier_option/docker/Dockerfile
new file mode 100644
index 00000000..5fca8567
--- /dev/null
+++ b/notebooks/asian_barrier_option/docker/Dockerfile
@@ -0,0 +1,39 @@
+FROM nvcr.io/nvidia/pytorch:19.10-py3
+USER root
+
+SHELL ["bash","-c"]
+
+#
+# Additional python libs
+#
+RUN pip install cupy-cuda101
+
+RUN conda install -y -c rapidsai -c nvidia -c conda-forge cudf=0.11 cudatoolkit=10.1 dask-cudf=0.11 dask-cuda=0.11 jupyterlab=0.35.4
+
+#RUN conda install -y -c pytorch ignite 
+RUN pip install pytorch-ignite
+
+RUN conda install -y nodejs
+
+RUN jupyter labextension install @ijmbarr/jupyterlab_spellchecker
+
+RUN mkdir /.local /.jupyter /.config /.cupy  && chmod 777 /.local /.jupyter /.config /.cupy
+
+# RUN cp -r /usr/lib/python3.6/dist-packages/tensorrt /opt/conda/lib/python3.6/site-packages/tensorrt
+# # Add TensorRT executable to path (trtexec)
+# ENV PATH=$PATH:/usr/src/tensorrt/bin
+
+
+# Here's a good place to install pip reqs from JoC repo.
+# At the same step, also install TRT pip reqs
+WORKDIR /tmp/pipReqs
+RUN pip install pycuda pillow
+# NeMo toolkit
+RUN pip install nemo-toolkit==0.9.0
+
+
+EXPOSE 8888
+EXPOSE 8787
+EXPOSE 8786
+
+WORKDIR /
diff --git a/notebooks/asian_barrier_option/download_data.sh b/notebooks/asian_barrier_option/download_data.sh
new file mode 100755
index 00000000..24412d0b
--- /dev/null
+++ b/notebooks/asian_barrier_option/download_data.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+DATA_PATH=./check_points
+mkdir -p $DATA_PATH
+mkdir -p $DATA_PATH/512/
+wget https://query.data.world/s/fb3ilrt77qcpx7kwnfgr3cybvdctk2 -O $DATA_PATH/model_best.pth.tar
+wget https://query.data.world/s/o2kzs74pg22mc2mfyhkykyu6pq36yr -O $DATA_PATH/512/model_best.pth.tar
diff --git a/notebooks/asian_barrier_option/elu_activation/CMakeLists.txt b/notebooks/asian_barrier_option/elu_activation/CMakeLists.txt
new file mode 100644
index 00000000..66a5431c
--- /dev/null
+++ b/notebooks/asian_barrier_option/elu_activation/CMakeLists.txt
@@ -0,0 +1,69 @@
+# Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+cmake_minimum_required(VERSION 3.8 FATAL_ERROR)
+project(cmake_and_cuda LANGUAGES CXX CUDA)
+
+set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS}  \
+--expt-relaxed-constexpr \
+--expt-extended-lambda \
+-gencode arch=compute_70,code=sm_70 \
+-gencode arch=compute_75,code=sm_75 \
+-Wno-deprecated-declarations")
+
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-deprecated-declarations")
+
+set(ELU_LIBS
+    cudart
+    cublas
+    nvinfer
+    nvinfer_plugin
+    pthread
+    z
+)
+
+include_directories(
+    ./
+    ./log
+    ./plugins
+    /usr/include/x86_64-linux-gnu
+    /usr/local/cuda-10.1/targets/x86_64-linux/include
+    /workspace/tensorrt/include
+    /workspace/tensorrt/samples/common
+    /opt/pytorch/pytorch/third_party/cub
+)
+
+link_directories(
+    /usr/lib/x86_64-linux-gnu
+    /usr/local/cuda-10.1/targets/x86_64-linux/lib
+    /workspace/tensorrt/lib
+)
+
+add_library(common SHARED
+    ./log/logger.cpp
+)
+
+add_library(my_plugins SHARED
+    plugins/eluPlugin.cu
+)
+
+target_link_libraries(my_plugins
+    common
+    ${ELU_LIBS}
+)
+
+target_link_libraries(common
+    ${ELU_LIBS}
+)
+
diff --git a/notebooks/asian_barrier_option/elu_activation/log/common.h b/notebooks/asian_barrier_option/elu_activation/log/common.h
new file mode 100644
index 00000000..f79ab49f
--- /dev/null
+++ b/notebooks/asian_barrier_option/elu_activation/log/common.h
@@ -0,0 +1,907 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef TENSORRT_COMMON_H
+#define TENSORRT_COMMON_H
+
+// For loadLibrary
+#ifdef _MSC_VER
+// Needed so that the max/min definitions in windows.h do not conflict with std::max/min.
+#define NOMINMAX
+#include <windows.h>
+#undef NOMINMAX
+#else
+#include <dlfcn.h>
+#endif
+
+#include "NvInfer.h"
+#include "NvInferPlugin.h"
+#include "logger.h"
+#include <algorithm>
+#include <cassert>
+#include <chrono>
+#include <cmath>
+#include <cstring>
+#include <cuda_runtime_api.h>
+#include <fstream>
+#include <iomanip>
+#include <iostream>
+#include <iterator>
+#include <map>
+#include <memory>
+#include <new>
+#include <numeric>
+#include <ratio>
+#include <sstream>
+#include <string>
+#include <utility>
+#include <vector>
+
+using namespace nvinfer1;
+using namespace plugin;
+
+#ifdef _MSC_VER
+#define FN_NAME __FUNCTION__
+#else
+#define FN_NAME __func__
+#endif
+
+#if (!defined(__ANDROID__) && defined(__aarch64__)) || defined(__QNX__)
+#define ENABLE_DLA_API 1
+#endif
+
+#define CHECK(status)                                                                                                  \
+    do                                                                                                                 \
+    {                                                                                                                  \
+        auto ret = (status);                                                                                           \
+        if (ret != 0)                                                                                                  \
+        {                                                                                                              \
+            std::cerr << "Cuda failure: " << ret << std::endl;                                                         \
+            abort();                                                                                                   \
+        }                                                                                                              \
+    } while (0)
+
+#define CHECK_RETURN_W_MSG(status, val, errMsg)                                                                        \
+    do                                                                                                                 \
+    {                                                                                                                  \
+        if (!(status))                                                                                                 \
+        {                                                                                                              \
+            std::cerr << errMsg << " Error in " << __FILE__ << ", function " << FN_NAME << "(), line " << __LINE__     \
+                      << std::endl;                                                                                    \
+            return val;                                                                                                \
+        }                                                                                                              \
+    } while (0)
+
+#define CHECK_RETURN(status, val) CHECK_RETURN_W_MSG(status, val, "")
+
+#define OBJ_GUARD(A) std::unique_ptr<A, void (*)(A * t)>
+
+template <typename T, typename T_>
+OBJ_GUARD(T)
+makeObjGuard(T_* t)
+{
+    CHECK(!(std::is_base_of<T, T_>::value || std::is_same<T, T_>::value));
+    auto deleter = [](T* t) { t->destroy(); };
+    return std::unique_ptr<T, decltype(deleter)>{static_cast<T*>(t), deleter};
+}
+
+constexpr long double operator"" _GiB(long double val)
+{
+    return val * (1 << 30);
+}
+constexpr long double operator"" _MiB(long double val)
+{
+    return val * (1 << 20);
+}
+constexpr long double operator"" _KiB(long double val)
+{
+    return val * (1 << 10);
+}
+
+// These is necessary if we want to be able to write 1_GiB instead of 1.0_GiB.
+// Since the return type is signed, -1_GiB will work as expected.
+constexpr long long int operator"" _GiB(long long unsigned int val)
+{
+    return val * (1 << 30);
+}
+constexpr long long int operator"" _MiB(long long unsigned int val)
+{
+    return val * (1 << 20);
+}
+constexpr long long int operator"" _KiB(long long unsigned int val)
+{
+    return val * (1 << 10);
+}
+
+struct SimpleProfiler : public nvinfer1::IProfiler
+{
+    struct Record
+    {
+        float time{0};
+        int count{0};
+    };
+
+    virtual void reportLayerTime(const char* layerName, float ms)
+    {
+        mProfile[layerName].count++;
+        mProfile[layerName].time += ms;
+        if (std::find(mLayerNames.begin(), mLayerNames.end(), layerName) == mLayerNames.end())
+        {
+            mLayerNames.push_back(layerName);
+        }
+    }
+
+    SimpleProfiler(const char* name, const std::vector<SimpleProfiler>& srcProfilers = std::vector<SimpleProfiler>())
+        : mName(name)
+    {
+        for (const auto& srcProfiler : srcProfilers)
+        {
+            for (const auto& rec : srcProfiler.mProfile)
+            {
+                auto it = mProfile.find(rec.first);
+                if (it == mProfile.end())
+                {
+                    mProfile.insert(rec);
+                }
+                else
+                {
+                    it->second.time += rec.second.time;
+                    it->second.count += rec.second.count;
+                }
+            }
+        }
+    }
+
+    friend std::ostream& operator<<(std::ostream& out, const SimpleProfiler& value)
+    {
+        out << "========== " << value.mName << " profile ==========" << std::endl;
+        float totalTime = 0;
+        std::string layerNameStr = "TensorRT layer name";
+        int maxLayerNameLength = std::max(static_cast<int>(layerNameStr.size()), 70);
+        for (const auto& elem : value.mProfile)
+        {
+            totalTime += elem.second.time;
+            maxLayerNameLength = std::max(maxLayerNameLength, static_cast<int>(elem.first.size()));
+        }
+
+        auto old_settings = out.flags();
+        auto old_precision = out.precision();
+        // Output header
+        {
+            out << std::setw(maxLayerNameLength) << layerNameStr << " ";
+            out << std::setw(12) << "Runtime, "
+                << "%"
+                << " ";
+            out << std::setw(12) << "Invocations"
+                << " ";
+            out << std::setw(12) << "Runtime, ms" << std::endl;
+        }
+        for (size_t i = 0; i < value.mLayerNames.size(); i++)
+        {
+            const std::string layerName = value.mLayerNames[i];
+            auto elem = value.mProfile.at(layerName);
+            out << std::setw(maxLayerNameLength) << layerName << " ";
+            out << std::setw(12) << std::fixed << std::setprecision(1) << (elem.time * 100.0F / totalTime) << "%"
+                << " ";
+            out << std::setw(12) << elem.count << " ";
+            out << std::setw(12) << std::fixed << std::setprecision(2) << elem.time << std::endl;
+        }
+        out.flags(old_settings);
+        out.precision(old_precision);
+        out << "========== " << value.mName << " total runtime = " << totalTime << " ms ==========" << std::endl;
+
+        return out;
+    }
+
+private:
+    std::string mName;
+    std::vector<std::string> mLayerNames;
+    std::map<std::string, Record> mProfile;
+};
+
+// Locate path to file, given its filename or filepath suffix and possible dirs it might lie in
+// Function will also walk back MAX_DEPTH dirs from CWD to check for such a file path
+inline std::string locateFile(const std::string& filepathSuffix, const std::vector<std::string>& directories)
+{
+    const int MAX_DEPTH{10};
+    bool found{false};
+    std::string filepath;
+
+    for (auto& dir : directories)
+    {
+        if (!dir.empty() && dir.back() != '/')
+        {
+#ifdef _MSC_VER
+            filepath = dir + "\\" + filepathSuffix;
+#else
+            filepath = dir + "/" + filepathSuffix;
+#endif
+        }
+        else
+            filepath = dir + filepathSuffix;
+
+        for (int i = 0; i < MAX_DEPTH && !found; i++)
+        {
+            std::ifstream checkFile(filepath);
+            found = checkFile.is_open();
+            if (found)
+                break;
+            filepath = "../" + filepath; // Try again in parent dir
+        }
+
+        if (found)
+        {
+            break;
+        }
+
+        filepath.clear();
+    }
+
+    if (filepath.empty())
+    {
+        std::string directoryList = std::accumulate(directories.begin() + 1, directories.end(), directories.front(),
+            [](const std::string& a, const std::string& b) { return a + "\n\t" + b; });
+        std::cout << "Could not find " << filepathSuffix << " in data directories:\n\t" << directoryList << std::endl;
+        std::cout << "&&&& FAILED" << std::endl;
+        exit(EXIT_FAILURE);
+    }
+    return filepath;
+}
+
+inline void readPGMFile(const std::string& fileName, uint8_t* buffer, int inH, int inW)
+{
+    std::ifstream infile(fileName, std::ifstream::binary);
+    assert(infile.is_open() && "Attempting to read from a file that is not open.");
+    std::string magic, h, w, max;
+    infile >> magic >> h >> w >> max;
+    infile.seekg(1, infile.cur);
+    infile.read(reinterpret_cast<char*>(buffer), inH * inW);
+}
+
+namespace samplesCommon
+{
+
+// Swaps endianness of an integral type.
+template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
+inline T swapEndianness(const T& value)
+{
+    uint8_t bytes[sizeof(T)];
+    for (int i = 0; i < static_cast<int>(sizeof(T)); ++i)
+    {
+        bytes[sizeof(T) - 1 - i] = *(reinterpret_cast<const uint8_t*>(&value) + i);
+    }
+    return *reinterpret_cast<T*>(bytes);
+}
+
+class HostMemory : public IHostMemory
+{
+public:
+    HostMemory() = delete;
+    void* data() const noexcept override
+    {
+        return mData;
+    }
+    std::size_t size() const noexcept override
+    {
+        return mSize;
+    }
+    DataType type() const noexcept override
+    {
+        return mType;
+    }
+
+protected:
+    HostMemory(std::size_t size, DataType type)
+        : mSize(size)
+        , mType(type)
+    {
+    }
+    void* mData;
+    std::size_t mSize;
+    DataType mType;
+};
+
+template <typename ElemType, DataType dataType>
+class TypedHostMemory : public HostMemory
+{
+public:
+    TypedHostMemory(std::size_t size)
+        : HostMemory(size, dataType)
+    {
+        mData = new ElemType[size];
+    };
+    void destroy() noexcept override
+    {
+        delete[](ElemType*) mData;
+        delete this;
+    }
+    ElemType* raw() noexcept
+    {
+        return static_cast<ElemType*>(data());
+    }
+};
+
+using FloatMemory = TypedHostMemory<float, DataType::kFLOAT>;
+using HalfMemory = TypedHostMemory<uint16_t, DataType::kHALF>;
+using ByteMemory = TypedHostMemory<uint8_t, DataType::kINT8>;
+
+inline void* safeCudaMalloc(size_t memSize)
+{
+    void* deviceMem;
+    CHECK(cudaMalloc(&deviceMem, memSize));
+    if (deviceMem == nullptr)
+    {
+        std::cerr << "Out of memory" << std::endl;
+        exit(1);
+    }
+    return deviceMem;
+}
+
+inline bool isDebug()
+{
+    return (std::getenv("TENSORRT_DEBUG") ? true : false);
+}
+
+struct InferDeleter
+{
+    template <typename T>
+    void operator()(T* obj) const
+    {
+        if (obj)
+        {
+            obj->destroy();
+        }
+    }
+};
+
+template <typename T>
+inline std::shared_ptr<T> infer_object(T* obj)
+{
+    if (!obj)
+    {
+        throw std::runtime_error("Failed to create object");
+    }
+    return std::shared_ptr<T>(obj, InferDeleter());
+}
+
+template <class Iter>
+inline std::vector<size_t> argsort(Iter begin, Iter end, bool reverse = false)
+{
+    std::vector<size_t> inds(end - begin);
+    std::iota(inds.begin(), inds.end(), 0);
+    if (reverse)
+    {
+        std::sort(inds.begin(), inds.end(), [&begin](size_t i1, size_t i2) { return begin[i2] < begin[i1]; });
+    }
+    else
+    {
+        std::sort(inds.begin(), inds.end(), [&begin](size_t i1, size_t i2) { return begin[i1] < begin[i2]; });
+    }
+    return inds;
+}
+
+inline bool readReferenceFile(const std::string& fileName, std::vector<std::string>& refVector)
+{
+    std::ifstream infile(fileName);
+    if (!infile.is_open())
+    {
+        std::cout << "ERROR: readReferenceFile: Attempting to read from a file that is not open." << std::endl;
+        return false;
+    }
+    std::string line;
+    while (std::getline(infile, line))
+    {
+        if (line.empty())
+            continue;
+        refVector.push_back(line);
+    }
+    infile.close();
+    return true;
+}
+
+template <typename result_vector_t>
+inline std::vector<std::string> classify(
+    const std::vector<std::string>& refVector, const result_vector_t& output, const size_t topK)
+{
+    auto inds = samplesCommon::argsort(output.cbegin(), output.cend(), true);
+    std::vector<std::string> result;
+    for (size_t k = 0; k < topK; ++k)
+    {
+        result.push_back(refVector[inds[k]]);
+    }
+    return result;
+}
+
+// Returns top K indices, not values.
+template <typename T>
+inline std::vector<size_t> topK(const std::vector<T> inp, const size_t k)
+{
+    std::vector<size_t> result;
+    std::vector<size_t> inds = samplesCommon::argsort(inp.cbegin(), inp.cend(), true);
+    result.assign(inds.begin(), inds.begin() + k);
+    return result;
+}
+
+template <typename T>
+inline bool readASCIIFile(const std::string& fileName, const size_t size, std::vector<T>& out)
+{
+    std::ifstream infile(fileName);
+    if (!infile.is_open())
+    {
+        std::cout << "ERROR readASCIIFile: Attempting to read from a file that is not open." << std::endl;
+        return false;
+    }
+    out.clear();
+    out.reserve(size);
+    out.assign(std::istream_iterator<T>(infile), std::istream_iterator<T>());
+    infile.close();
+    return true;
+}
+
+template <typename T>
+inline bool writeASCIIFile(const std::string& fileName, const std::vector<T>& in)
+{
+    std::ofstream outfile(fileName);
+    if (!outfile.is_open())
+    {
+        std::cout << "ERROR: writeASCIIFile: Attempting to write to a file that is not open." << std::endl;
+        return false;
+    }
+    for (auto fn : in)
+    {
+        outfile << fn << "\n";
+    }
+    outfile.close();
+    return true;
+}
+
+inline void print_version()
+{
+    std::cout << "  TensorRT version: " << NV_TENSORRT_MAJOR << "." << NV_TENSORRT_MINOR << "." << NV_TENSORRT_PATCH
+              << "." << NV_TENSORRT_BUILD << std::endl;
+}
+
+inline std::string getFileType(const std::string& filepath)
+{
+    return filepath.substr(filepath.find_last_of(".") + 1);
+}
+
+inline std::string toLower(const std::string& inp)
+{
+    std::string out = inp;
+    std::transform(out.begin(), out.end(), out.begin(), ::tolower);
+    return out;
+}
+
+inline float getMaxValue(const float* buffer, int64_t size)
+{
+    assert(buffer != nullptr);
+    assert(size > 0);
+    return *std::max_element(buffer, buffer + size);
+}
+
+// Ensures that every tensor used by a network has a scale.
+//
+// All tensors in a network must have a range specified if a calibrator is not used.
+// This function is just a utility to globally fill in missing scales for the entire network.
+//
+// If a tensor does not have a scale, it is assigned inScales or outScales as follows:
+//
+// * If the tensor is the input to a layer or output of a pooling node, its scale is assigned inScales.
+// * Otherwise its scale is assigned outScales.
+//
+// The default parameter values are intended to demonstrate, for final layers in the network,
+// cases where scaling factors are asymmetric.
+inline void setAllTensorScales(INetworkDefinition* network, float inScales = 2.0f, float outScales = 4.0f)
+{
+    // Ensure that all layer inputs have a scale.
+    for (int i = 0; i < network->getNbLayers(); i++)
+    {
+        auto layer = network->getLayer(i);
+        for (int j = 0; j < layer->getNbInputs(); j++)
+        {
+            ITensor* input{layer->getInput(j)};
+            // Optional inputs are nullptr here and are from RNN layers.
+            if (input != nullptr && !input->dynamicRangeIsSet())
+            {
+                input->setDynamicRange(-inScales, inScales);
+            }
+        }
+    }
+
+    // Ensure that all layer outputs have a scale.
+    // Tensors that are also inputs to layers are ingored here
+    // since the previous loop nest assigned scales to them.
+    for (int i = 0; i < network->getNbLayers(); i++)
+    {
+        auto layer = network->getLayer(i);
+        for (int j = 0; j < layer->getNbOutputs(); j++)
+        {
+            ITensor* output{layer->getOutput(j)};
+            // Optional outputs are nullptr here and are from RNN layers.
+            if (output != nullptr && !output->dynamicRangeIsSet())
+            {
+                // Pooling must have the same input and output scales.
+                if (layer->getType() == LayerType::kPOOLING)
+                {
+                    output->setDynamicRange(-inScales, inScales);
+                }
+                else
+                {
+                    output->setDynamicRange(-outScales, outScales);
+                }
+            }
+        }
+    }
+}
+
+inline void setDummyInt8Scales(const IBuilderConfig* c, INetworkDefinition* n)
+{
+    // Set dummy tensor scales if Int8 mode is requested.
+    if (c->getFlag(BuilderFlag::kINT8))
+    {
+        gLogWarning
+            << "Int8 calibrator not provided. Generating dummy per tensor scales. Int8 accuracy is not guaranteed."
+            << std::endl;
+        setAllTensorScales(n);
+    }
+}
+
+inline void enableDLA(IBuilder* builder, IBuilderConfig* config, int useDLACore, bool allowGPUFallback = true)
+{
+    if (useDLACore >= 0)
+    {
+        if (builder->getNbDLACores() == 0)
+        {
+            std::cerr << "Trying to use DLA core " << useDLACore << " on a platform that doesn't have any DLA cores"
+                      << std::endl;
+            assert("Error: use DLA core on a platfrom that doesn't have any DLA cores" && false);
+        }
+        if (allowGPUFallback)
+        {
+            config->setFlag(BuilderFlag::kGPU_FALLBACK);
+        }
+        if (!builder->getInt8Mode() && !config->getFlag(BuilderFlag::kINT8))
+        {
+            // User has not requested INT8 Mode.
+            // By default run in FP16 mode. FP32 mode is not permitted.
+            builder->setFp16Mode(true);
+            config->setFlag(BuilderFlag::kFP16);
+        }
+        config->setDefaultDeviceType(DeviceType::kDLA);
+        config->setDLACore(useDLACore);
+        config->setFlag(BuilderFlag::kSTRICT_TYPES);
+    }
+}
+
+inline int parseDLA(int argc, char** argv)
+{
+    for (int i = 1; i < argc; i++)
+    {
+        std::string arg(argv[i]);
+        if (strncmp(argv[i], "--useDLACore=", 13) == 0)
+            return std::stoi(argv[i] + 13);
+    }
+    return -1;
+}
+
+inline unsigned int getElementSize(nvinfer1::DataType t)
+{
+    switch (t)
+    {
+    case nvinfer1::DataType::kINT32: return 4;
+    case nvinfer1::DataType::kFLOAT: return 4;
+    case nvinfer1::DataType::kHALF: return 2;
+    case nvinfer1::DataType::kINT8: return 1;
+    }
+    throw std::runtime_error("Invalid DataType.");
+    return 0;
+}
+
+inline int64_t volume(const nvinfer1::Dims& d)
+{
+    return std::accumulate(d.d, d.d + d.nbDims, 1, std::multiplies<int64_t>());
+}
+
+inline unsigned int elementSize(DataType t)
+{
+    switch (t)
+    {
+    case DataType::kINT32:
+    case DataType::kFLOAT: return 4;
+    case DataType::kHALF: return 2;
+    case DataType::kINT8: return 1;
+    }
+    return 0;
+}
+
+template <typename A, typename B>
+inline A divUp(A x, B n)
+{
+    return (x + n - 1) / n;
+}
+
+template <int C, int H, int W>
+struct PPM
+{
+    std::string magic, fileName;
+    int h, w, max;
+    uint8_t buffer[C * H * W];
+};
+
+// New vPPM(variable sized PPM) class with variable dimensions.
+struct vPPM
+{
+    std::string magic, fileName;
+    int h, w, max;
+    std::vector<uint8_t> buffer;
+};
+
+struct BBox
+{
+    float x1, y1, x2, y2;
+};
+
+template <int C, int H, int W>
+inline void readPPMFile(const std::string& filename, samplesCommon::PPM<C, H, W>& ppm)
+{
+    ppm.fileName = filename;
+    std::ifstream infile(filename, std::ifstream::binary);
+    assert(infile.is_open() && "Attempting to read from a file that is not open.");
+    infile >> ppm.magic >> ppm.w >> ppm.h >> ppm.max;
+    infile.seekg(1, infile.cur);
+    infile.read(reinterpret_cast<char*>(ppm.buffer), ppm.w * ppm.h * 3);
+}
+
+inline void readPPMFile(const std::string& filename, vPPM& ppm, std::vector<std::string>& input_dir)
+{
+    ppm.fileName = filename;
+    std::ifstream infile(locateFile(filename, input_dir), std::ifstream::binary);
+    infile >> ppm.magic >> ppm.w >> ppm.h >> ppm.max;
+    infile.seekg(1, infile.cur);
+
+    for (int i = 0; i < ppm.w * ppm.h * 3; ++i)
+    {
+        ppm.buffer.push_back(0);
+    }
+
+    infile.read(reinterpret_cast<char*>(&ppm.buffer[0]), ppm.w * ppm.h * 3);
+}
+
+template <int C, int H, int W>
+inline void writePPMFileWithBBox(const std::string& filename, PPM<C, H, W>& ppm, const BBox& bbox)
+{
+    std::ofstream outfile("./" + filename, std::ofstream::binary);
+    assert(!outfile.fail());
+    outfile << "P6"
+            << "\n"
+            << ppm.w << " " << ppm.h << "\n"
+            << ppm.max << "\n";
+    auto round = [](float x) -> int { return int(std::floor(x + 0.5f)); };
+    const int x1 = std::min(std::max(0, round(int(bbox.x1))), W - 1);
+    const int x2 = std::min(std::max(0, round(int(bbox.x2))), W - 1);
+    const int y1 = std::min(std::max(0, round(int(bbox.y1))), H - 1);
+    const int y2 = std::min(std::max(0, round(int(bbox.y2))), H - 1);
+    for (int x = x1; x <= x2; ++x)
+    {
+        // bbox top border
+        ppm.buffer[(y1 * ppm.w + x) * 3] = 255;
+        ppm.buffer[(y1 * ppm.w + x) * 3 + 1] = 0;
+        ppm.buffer[(y1 * ppm.w + x) * 3 + 2] = 0;
+        // bbox bottom border
+        ppm.buffer[(y2 * ppm.w + x) * 3] = 255;
+        ppm.buffer[(y2 * ppm.w + x) * 3 + 1] = 0;
+        ppm.buffer[(y2 * ppm.w + x) * 3 + 2] = 0;
+    }
+    for (int y = y1; y <= y2; ++y)
+    {
+        // bbox left border
+        ppm.buffer[(y * ppm.w + x1) * 3] = 255;
+        ppm.buffer[(y * ppm.w + x1) * 3 + 1] = 0;
+        ppm.buffer[(y * ppm.w + x1) * 3 + 2] = 0;
+        // bbox right border
+        ppm.buffer[(y * ppm.w + x2) * 3] = 255;
+        ppm.buffer[(y * ppm.w + x2) * 3 + 1] = 0;
+        ppm.buffer[(y * ppm.w + x2) * 3 + 2] = 0;
+    }
+    outfile.write(reinterpret_cast<char*>(ppm.buffer), ppm.w * ppm.h * 3);
+}
+
+inline void writePPMFileWithBBox(const std::string& filename, vPPM ppm, std::vector<BBox>& dets)
+{
+    std::ofstream outfile("./" + filename, std::ofstream::binary);
+    assert(!outfile.fail());
+    outfile << "P6"
+            << "\n"
+            << ppm.w << " " << ppm.h << "\n"
+            << ppm.max << "\n";
+    auto round = [](float x) -> int { return int(std::floor(x + 0.5f)); };
+
+    for (auto bbox : dets)
+    {
+        for (int x = int(bbox.x1); x < int(bbox.x2); ++x)
+        {
+            // bbox top border
+            ppm.buffer[(round(bbox.y1) * ppm.w + x) * 3] = 255;
+            ppm.buffer[(round(bbox.y1) * ppm.w + x) * 3 + 1] = 0;
+            ppm.buffer[(round(bbox.y1) * ppm.w + x) * 3 + 2] = 0;
+            // bbox bottom border
+            ppm.buffer[(round(bbox.y2) * ppm.w + x) * 3] = 255;
+            ppm.buffer[(round(bbox.y2) * ppm.w + x) * 3 + 1] = 0;
+            ppm.buffer[(round(bbox.y2) * ppm.w + x) * 3 + 2] = 0;
+        }
+
+        for (int y = int(bbox.y1); y < int(bbox.y2); ++y)
+        {
+            // bbox left border
+            ppm.buffer[(y * ppm.w + round(bbox.x1)) * 3] = 255;
+            ppm.buffer[(y * ppm.w + round(bbox.x1)) * 3 + 1] = 0;
+            ppm.buffer[(y * ppm.w + round(bbox.x1)) * 3 + 2] = 0;
+            // bbox right border
+            ppm.buffer[(y * ppm.w + round(bbox.x2)) * 3] = 255;
+            ppm.buffer[(y * ppm.w + round(bbox.x2)) * 3 + 1] = 0;
+            ppm.buffer[(y * ppm.w + round(bbox.x2)) * 3 + 2] = 0;
+        }
+    }
+
+    outfile.write(reinterpret_cast<char*>(&ppm.buffer[0]), ppm.w * ppm.h * 3);
+}
+
+class TimerBase
+{
+public:
+    virtual void start() {}
+    virtual void stop() {}
+    float microseconds() const noexcept
+    {
+        return mMs * 1000.f;
+    }
+    float milliseconds() const noexcept
+    {
+        return mMs;
+    }
+    float seconds() const noexcept
+    {
+        return mMs / 1000.f;
+    }
+    void reset() noexcept
+    {
+        mMs = 0.f;
+    }
+
+protected:
+    float mMs{0.0f};
+};
+
+class GpuTimer : public TimerBase
+{
+public:
+    GpuTimer(cudaStream_t stream)
+        : mStream(stream)
+    {
+        CHECK(cudaEventCreate(&mStart));
+        CHECK(cudaEventCreate(&mStop));
+    }
+    ~GpuTimer()
+    {
+        CHECK(cudaEventDestroy(mStart));
+        CHECK(cudaEventDestroy(mStop));
+    }
+    void start()
+    {
+        CHECK(cudaEventRecord(mStart, mStream));
+    }
+    void stop()
+    {
+        CHECK(cudaEventRecord(mStop, mStream));
+        float ms{0.0f};
+        CHECK(cudaEventSynchronize(mStop));
+        CHECK(cudaEventElapsedTime(&ms, mStart, mStop));
+        mMs += ms;
+    }
+
+private:
+    cudaEvent_t mStart, mStop;
+    cudaStream_t mStream;
+}; // class GpuTimer
+
+template <typename Clock>
+class CpuTimer : public TimerBase
+{
+public:
+    using clock_type = Clock;
+
+    void start()
+    {
+        mStart = Clock::now();
+    }
+    void stop()
+    {
+        mStop = Clock::now();
+        mMs += std::chrono::duration<float, std::milli>{mStop - mStart}.count();
+    }
+
+private:
+    std::chrono::time_point<Clock> mStart, mStop;
+}; // class CpuTimer
+
+using PreciseCpuTimer = CpuTimer<std::chrono::high_resolution_clock>;
+
+inline std::vector<std::string> splitString(std::string str, char delimiter = ',')
+{
+    std::vector<std::string> splitVect;
+    std::stringstream ss(str);
+    std::string substr;
+
+    while (ss.good())
+    {
+        getline(ss, substr, delimiter);
+        splitVect.emplace_back(std::move(substr));
+    }
+    return splitVect;
+}
+
+// Return m rounded up to nearest multiple of n
+inline int roundUp(int m, int n)
+{
+    return ((m + n - 1) / n) * n;
+}
+
+inline int getC(const Dims& d)
+{
+    return d.nbDims >= 3 ? d.d[d.nbDims - 3] : 1;
+}
+
+inline int getH(const Dims& d)
+{
+    return d.nbDims >= 2 ? d.d[d.nbDims - 2] : 1;
+}
+
+inline int getW(const Dims& d)
+{
+    return d.nbDims >= 1 ? d.d[d.nbDims - 1] : 1;
+}
+
+inline void loadLibrary(const std::string& path)
+{
+#ifdef _MSC_VER
+    void* handle = LoadLibrary(path.c_str());
+#else
+    void* handle = dlopen(path.c_str(), RTLD_LAZY);
+#endif
+    if (handle == nullptr)
+    {
+#ifdef _MSC_VER
+        gLogError << "Could not load plugin library: " << path << std::endl;
+#else
+        gLogError << "Could not load plugin library: " << path << ", due to: " << dlerror() << std::endl;
+#endif
+    }
+}
+
+} // namespace samplesCommon
+
+inline std::ostream& operator<<(std::ostream& os, const nvinfer1::Dims& dims)
+{
+    os << "(";
+    for (int i = 0; i < dims.nbDims; ++i)
+    {
+        os << (i ? ", " : "") << dims.d[i];
+    }
+    return os << ")";
+}
+
+#endif // TENSORRT_COMMON_H
diff --git a/notebooks/asian_barrier_option/elu_activation/log/logger.cpp b/notebooks/asian_barrier_option/elu_activation/log/logger.cpp
new file mode 100644
index 00000000..acbef64c
--- /dev/null
+++ b/notebooks/asian_barrier_option/elu_activation/log/logger.cpp
@@ -0,0 +1,35 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "logger.h"
+#include "logging.h"
+
+Logger gLogger{Logger::Severity::kINFO};
+LogStreamConsumer gLogVerbose{LOG_VERBOSE(gLogger)};
+LogStreamConsumer gLogInfo{LOG_INFO(gLogger)};
+LogStreamConsumer gLogWarning{LOG_WARN(gLogger)};
+LogStreamConsumer gLogError{LOG_ERROR(gLogger)};
+LogStreamConsumer gLogFatal{LOG_FATAL(gLogger)};
+
+void setReportableSeverity(Logger::Severity severity)
+{
+    gLogger.setReportableSeverity(severity);
+    gLogVerbose.setReportableSeverity(severity);
+    gLogInfo.setReportableSeverity(severity);
+    gLogWarning.setReportableSeverity(severity);
+    gLogError.setReportableSeverity(severity);
+    gLogFatal.setReportableSeverity(severity);
+}
diff --git a/notebooks/asian_barrier_option/elu_activation/log/logger.h b/notebooks/asian_barrier_option/elu_activation/log/logger.h
new file mode 100644
index 00000000..e9cafa19
--- /dev/null
+++ b/notebooks/asian_barrier_option/elu_activation/log/logger.h
@@ -0,0 +1,31 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef LOGGER_H
+#define LOGGER_H
+
+#include "logging.h"
+
+extern Logger gLogger;
+extern LogStreamConsumer gLogVerbose;
+extern LogStreamConsumer gLogInfo;
+extern LogStreamConsumer gLogWarning;
+extern LogStreamConsumer gLogError;
+extern LogStreamConsumer gLogFatal;
+
+void setReportableSeverity(Logger::Severity severity);
+
+#endif // LOGGER_H
diff --git a/notebooks/asian_barrier_option/elu_activation/log/logging.h b/notebooks/asian_barrier_option/elu_activation/log/logging.h
new file mode 100644
index 00000000..63a0a3a1
--- /dev/null
+++ b/notebooks/asian_barrier_option/elu_activation/log/logging.h
@@ -0,0 +1,503 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef TENSORRT_LOGGING_H
+#define TENSORRT_LOGGING_H
+
+#include "NvInferRuntimeCommon.h"
+#include <cassert>
+#include <ctime>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <sstream>
+#include <string>
+
+using Severity = nvinfer1::ILogger::Severity;
+
+class LogStreamConsumerBuffer : public std::stringbuf
+{
+public:
+    LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)
+        : mOutput(stream)
+        , mPrefix(prefix)
+        , mShouldLog(shouldLog)
+    {
+    }
+
+    LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other)
+        : mOutput(other.mOutput)
+    {
+    }
+
+    ~LogStreamConsumerBuffer()
+    {
+        // std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence
+        // std::streambuf::pptr() gives a pointer to the current position of the output sequence
+        // if the pointer to the beginning is not equal to the pointer to the current position,
+        // call putOutput() to log the output to the stream
+        if (pbase() != pptr())
+        {
+            putOutput();
+        }
+    }
+
+    // synchronizes the stream buffer and returns 0 on success
+    // synchronizing the stream buffer consists of inserting the buffer contents into the stream,
+    // resetting the buffer and flushing the stream
+    virtual int sync()
+    {
+        putOutput();
+        return 0;
+    }
+
+    void putOutput()
+    {
+        if (mShouldLog)
+        {
+            // prepend timestamp
+            std::time_t timestamp = std::time(nullptr);
+            tm* tm_local = std::localtime(&timestamp);
+            std::cout << "[";
+            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mon << "/";
+            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << "/";
+            std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << "-";
+            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << ":";
+            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":";
+            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << "] ";
+            // std::stringbuf::str() gets the string contents of the buffer
+            // insert the buffer contents pre-appended by the appropriate prefix into the stream
+            mOutput << mPrefix << str();
+            // set the buffer to empty
+            str("");
+            // flush the stream
+            mOutput.flush();
+        }
+    }
+
+    void setShouldLog(bool shouldLog)
+    {
+        mShouldLog = shouldLog;
+    }
+
+private:
+    std::ostream& mOutput;
+    std::string mPrefix;
+    bool mShouldLog;
+};
+
+//!
+//! \class LogStreamConsumerBase
+//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer
+//!
+class LogStreamConsumerBase
+{
+public:
+    LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)
+        : mBuffer(stream, prefix, shouldLog)
+    {
+    }
+
+protected:
+    LogStreamConsumerBuffer mBuffer;
+};
+
+//!
+//! \class LogStreamConsumer
+//! \brief Convenience object used to facilitate use of C++ stream syntax when logging messages.
+//!  Order of base classes is LogStreamConsumerBase and then std::ostream.
+//!  This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field
+//!  in LogStreamConsumer and then the address of the buffer is passed to std::ostream.
+//!  This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.
+//!  Please do not change the order of the parent classes.
+//!
+class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream
+{
+public:
+    //! \brief Creates a LogStreamConsumer which logs messages with level severity.
+    //!  Reportable severity determines if the messages are severe enough to be logged.
+    LogStreamConsumer(Severity reportableSeverity, Severity severity)
+        : LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity)
+        , std::ostream(&mBuffer) // links the stream buffer with the stream
+        , mShouldLog(severity <= reportableSeverity)
+        , mSeverity(severity)
+    {
+    }
+
+    LogStreamConsumer(LogStreamConsumer&& other)
+        : LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog)
+        , std::ostream(&mBuffer) // links the stream buffer with the stream
+        , mShouldLog(other.mShouldLog)
+        , mSeverity(other.mSeverity)
+    {
+    }
+
+    void setReportableSeverity(Severity reportableSeverity)
+    {
+        mShouldLog = mSeverity <= reportableSeverity;
+        mBuffer.setShouldLog(mShouldLog);
+    }
+
+private:
+    static std::ostream& severityOstream(Severity severity)
+    {
+        return severity >= Severity::kINFO ? std::cout : std::cerr;
+    }
+
+    static std::string severityPrefix(Severity severity)
+    {
+        switch (severity)
+        {
+        case Severity::kINTERNAL_ERROR: return "[F] ";
+        case Severity::kERROR: return "[E] ";
+        case Severity::kWARNING: return "[W] ";
+        case Severity::kINFO: return "[I] ";
+        case Severity::kVERBOSE: return "[V] ";
+        default: assert(0); return "";
+        }
+    }
+
+    bool mShouldLog;
+    Severity mSeverity;
+};
+
+//! \class Logger
+//!
+//! \brief Class which manages logging of TensorRT tools and samples
+//!
+//! \details This class provides a common interface for TensorRT tools and samples to log information to the console,
+//! and supports logging two types of messages:
+//!
+//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal)
+//! - Test pass/fail messages
+//!
+//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is
+//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location.
+//!
+//! In the future, this class could be extended to support dumping test results to a file in some standard format
+//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run).
+//!
+//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger
+//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT
+//! library and messages coming from the sample.
+//!
+//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the
+//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger
+//! object.
+
+class Logger : public nvinfer1::ILogger
+{
+public:
+    Logger(Severity severity = Severity::kWARNING)
+        : mReportableSeverity(severity)
+    {
+    }
+
+    //!
+    //! \enum TestResult
+    //! \brief Represents the state of a given test
+    //!
+    enum class TestResult
+    {
+        kRUNNING, //!< The test is running
+        kPASSED,  //!< The test passed
+        kFAILED,  //!< The test failed
+        kWAIVED   //!< The test was waived
+    };
+
+    //!
+    //! \brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger
+    //! \return The nvinfer1::ILogger associated with this Logger
+    //!
+    //! TODO Once all samples are updated to use this method to register the logger with TensorRT,
+    //! we can eliminate the inheritance of Logger from ILogger
+    //!
+    nvinfer1::ILogger& getTRTLogger()
+    {
+        return *this;
+    }
+
+    //!
+    //! \brief Implementation of the nvinfer1::ILogger::log() virtual method
+    //!
+    //! Note samples should not be calling this function directly; it will eventually go away once we eliminate the
+    //! inheritance from nvinfer1::ILogger
+    //!
+    void log(Severity severity, const char* msg) override
+    {
+        LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl;
+    }
+
+    //!
+    //! \brief Method for controlling the verbosity of logging output
+    //!
+    //! \param severity The logger will only emit messages that have severity of this level or higher.
+    //!
+    void setReportableSeverity(Severity severity)
+    {
+        mReportableSeverity = severity;
+    }
+
+    //!
+    //! \brief Opaque handle that holds logging information for a particular test
+    //!
+    //! This object is an opaque handle to information used by the Logger to print test results.
+    //! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used
+    //! with Logger::reportTest{Start,End}().
+    //!
+    class TestAtom
+    {
+    public:
+        TestAtom(TestAtom&&) = default;
+
+    private:
+        friend class Logger;
+
+        TestAtom(bool started, const std::string& name, const std::string& cmdline)
+            : mStarted(started)
+            , mName(name)
+            , mCmdline(cmdline)
+        {
+        }
+
+        bool mStarted;
+        std::string mName;
+        std::string mCmdline;
+    };
+
+    //!
+    //! \brief Define a test for logging
+    //!
+    //! \param[in] name The name of the test.  This should be a string starting with
+    //!                  "TensorRT" and containing dot-separated strings containing
+    //!                  the characters [A-Za-z0-9_].
+    //!                  For example, "TensorRT.sample_googlenet"
+    //! \param[in] cmdline The command line used to reproduce the test
+    //
+    //! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
+    //!
+    static TestAtom defineTest(const std::string& name, const std::string& cmdline)
+    {
+        return TestAtom(false, name, cmdline);
+    }
+
+    //!
+    //! \brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments
+    //!        as input
+    //!
+    //! \param[in] name The name of the test
+    //! \param[in] argc The number of command-line arguments
+    //! \param[in] argv The array of command-line arguments (given as C strings)
+    //!
+    //! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
+    static TestAtom defineTest(const std::string& name, int argc, char const* const* argv)
+    {
+        auto cmdline = genCmdlineString(argc, argv);
+        return defineTest(name, cmdline);
+    }
+
+    //!
+    //! \brief Report that a test has started.
+    //!
+    //! \pre reportTestStart() has not been called yet for the given testAtom
+    //!
+    //! \param[in] testAtom The handle to the test that has started
+    //!
+    static void reportTestStart(TestAtom& testAtom)
+    {
+        reportTestResult(testAtom, TestResult::kRUNNING);
+        assert(!testAtom.mStarted);
+        testAtom.mStarted = true;
+    }
+
+    //!
+    //! \brief Report that a test has ended.
+    //!
+    //! \pre reportTestStart() has been called for the given testAtom
+    //!
+    //! \param[in] testAtom The handle to the test that has ended
+    //! \param[in] result The result of the test. Should be one of TestResult::kPASSED,
+    //!                   TestResult::kFAILED, TestResult::kWAIVED
+    //!
+    static void reportTestEnd(const TestAtom& testAtom, TestResult result)
+    {
+        assert(result != TestResult::kRUNNING);
+        assert(testAtom.mStarted);
+        reportTestResult(testAtom, result);
+    }
+
+    static int reportPass(const TestAtom& testAtom)
+    {
+        reportTestEnd(testAtom, TestResult::kPASSED);
+        return EXIT_SUCCESS;
+    }
+
+    static int reportFail(const TestAtom& testAtom)
+    {
+        reportTestEnd(testAtom, TestResult::kFAILED);
+        return EXIT_FAILURE;
+    }
+
+    static int reportWaive(const TestAtom& testAtom)
+    {
+        reportTestEnd(testAtom, TestResult::kWAIVED);
+        return EXIT_SUCCESS;
+    }
+
+    static int reportTest(const TestAtom& testAtom, bool pass)
+    {
+        return pass ? reportPass(testAtom) : reportFail(testAtom);
+    }
+
+    Severity getReportableSeverity() const
+    {
+        return mReportableSeverity;
+    }
+
+private:
+    //!
+    //! \brief returns an appropriate string for prefixing a log message with the given severity
+    //!
+    static const char* severityPrefix(Severity severity)
+    {
+        switch (severity)
+        {
+        case Severity::kINTERNAL_ERROR: return "[F] ";
+        case Severity::kERROR: return "[E] ";
+        case Severity::kWARNING: return "[W] ";
+        case Severity::kINFO: return "[I] ";
+        case Severity::kVERBOSE: return "[V] ";
+        default: assert(0); return "";
+        }
+    }
+
+    //!
+    //! \brief returns an appropriate string for prefixing a test result message with the given result
+    //!
+    static const char* testResultString(TestResult result)
+    {
+        switch (result)
+        {
+        case TestResult::kRUNNING: return "RUNNING";
+        case TestResult::kPASSED: return "PASSED";
+        case TestResult::kFAILED: return "FAILED";
+        case TestResult::kWAIVED: return "WAIVED";
+        default: assert(0); return "";
+        }
+    }
+
+    //!
+    //! \brief returns an appropriate output stream (cout or cerr) to use with the given severity
+    //!
+    static std::ostream& severityOstream(Severity severity)
+    {
+        return severity >= Severity::kINFO ? std::cout : std::cerr;
+    }
+
+    //!
+    //! \brief method that implements logging test results
+    //!
+    static void reportTestResult(const TestAtom& testAtom, TestResult result)
+    {
+        severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # "
+                                         << testAtom.mCmdline << std::endl;
+    }
+
+    //!
+    //! \brief generate a command line string from the given (argc, argv) values
+    //!
+    static std::string genCmdlineString(int argc, char const* const* argv)
+    {
+        std::stringstream ss;
+        for (int i = 0; i < argc; i++)
+        {
+            if (i > 0)
+                ss << " ";
+            ss << argv[i];
+        }
+        return ss.str();
+    }
+
+    Severity mReportableSeverity;
+};
+
+namespace
+{
+
+//!
+//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE
+//!
+//! Example usage:
+//!
+//!     LOG_VERBOSE(logger) << "hello world" << std::endl;
+//!
+inline LogStreamConsumer LOG_VERBOSE(const Logger& logger)
+{
+    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);
+}
+
+//!
+//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO
+//!
+//! Example usage:
+//!
+//!     LOG_INFO(logger) << "hello world" << std::endl;
+//!
+inline LogStreamConsumer LOG_INFO(const Logger& logger)
+{
+    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);
+}
+
+//!
+//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING
+//!
+//! Example usage:
+//!
+//!     LOG_WARN(logger) << "hello world" << std::endl;
+//!
+inline LogStreamConsumer LOG_WARN(const Logger& logger)
+{
+    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);
+}
+
+//!
+//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR
+//!
+//! Example usage:
+//!
+//!     LOG_ERROR(logger) << "hello world" << std::endl;
+//!
+inline LogStreamConsumer LOG_ERROR(const Logger& logger)
+{
+    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);
+}
+
+//!
+//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR
+//         ("fatal" severity)
+//!
+//! Example usage:
+//!
+//!     LOG_FATAL(logger) << "hello world" << std::endl;
+//!
+inline LogStreamConsumer LOG_FATAL(const Logger& logger)
+{
+    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);
+}
+
+} // anonymous namespace
+
+#endif // TENSORRT_LOGGING_H
diff --git a/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.cu b/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.cu
new file mode 100644
index 00000000..83d67295
--- /dev/null
+++ b/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.cu
@@ -0,0 +1,292 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cassert>
+#include <cstring>
+#include <vector>
+
+#include "NvInfer.h"
+#include "eluPlugin.h"
+#include "pluginKernels.h"
+#include "common.h"
+#include "logger.h"
+
+using namespace nvinfer1;
+
+namespace elu
+{
+
+// constants for approximating the normal cdf
+constexpr float A = 0.0;
+constexpr float B = 1.0; // sqrt(2.0/M_PI)
+
+template <typename T, unsigned TPB>
+__global__ void eluKernel(const T a, const T b, int n, const T* input, T* output)
+{
+
+    const int idx = blockIdx.x * TPB + threadIdx.x;
+
+    if (idx < n)
+    {
+        const T in = input[idx];
+        const T tmp = exp(in) - b;
+        const T result = (a > in ? a : in) + (a < tmp ? a : tmp);
+        output[idx] = result;
+    }
+}
+
+inline int computeElu(cudaStream_t stream, int n, const float* input, float* output)
+{
+
+    constexpr int blockSize = 256;
+    const int gridSize = (n + blockSize - 1) / blockSize;
+    eluKernel<float, blockSize><<<gridSize, blockSize, 0, stream>>>(A, B, n, input, output);
+
+    CHECK(cudaPeekAtLastError());
+    return 0;
+}
+
+inline int computeElu(cudaStream_t stream, int n, const half* input, half* output)
+{
+    const int blockSize = 256;
+
+    if (0 == (n & 1))
+    {
+        const int n2 = n / 2;
+
+        const int gridSize = (n2 + blockSize - 1) / blockSize;
+        const half2 A2 = __floats2half2_rn(A, A);
+        const half2 B2 = __floats2half2_rn(B, B);
+        const half2* input2 = reinterpret_cast<const half2*>(input);
+        half2* output2 = reinterpret_cast<half2*>(output);
+        eluKernel<half2, blockSize><<<gridSize, blockSize, 0, stream>>>(A2, B2, n2, input2, output2);
+    }
+    else
+    {
+        const int gridSize = (n + blockSize - 1) / blockSize;
+        eluKernel<half, blockSize><<<gridSize, blockSize, 0, stream>>>(A, B, n, input, output);
+    }
+
+    CHECK(cudaPeekAtLastError());
+    return 0;
+}
+
+namespace
+{
+static const char* GELU_PLUGIN_VERSION{"1"};
+static const char* GELU_PLUGIN_NAME{"CustomEluPluginDynamic"};
+} // namespace
+
+// Static class fields initialization
+PluginFieldCollection EluPluginDynamicCreator::mFC{};
+std::vector<PluginField> EluPluginDynamicCreator::mPluginAttributes;
+
+REGISTER_TENSORRT_PLUGIN(EluPluginDynamicCreator);
+
+EluPluginDynamic::EluPluginDynamic(const std::string name)
+    : mLayerName(name)
+{
+}
+
+EluPluginDynamic::EluPluginDynamic(const std::string name, const void* data, size_t length)
+    : mLayerName(name)
+{
+
+    gLogVerbose << "Elu Deser start" << std::endl;
+    const char* d = static_cast<const char*>(data);
+    const char* a = d;
+    mType = readFromBuffer<DataType>(d);
+    assert(d == a + length);
+    gLogVerbose << "Elu Deser done" << std::endl;
+}
+// IPluginV2DynamicExt Methods
+nvinfer1::IPluginV2DynamicExt* EluPluginDynamic::clone() const
+{
+    return new EluPluginDynamic(mLayerName);
+}
+
+nvinfer1::DimsExprs EluPluginDynamic::getOutputDimensions(int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs, nvinfer1::IExprBuilder& exprBuilder)
+{
+    return inputs[0];
+}
+
+bool EluPluginDynamic::supportsFormatCombination(int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs)
+{
+
+    const PluginTensorDesc& input = inOut[0];
+    if (pos == 0)
+    {
+        return (input.type == DataType::kFLOAT || input.type == DataType::kHALF)
+            && (input.format == TensorFormat::kLINEAR);
+    }
+    if (pos == 1)
+    {
+        const PluginTensorDesc& output = inOut[1];
+        return (input.type == output.type) && (output.format == TensorFormat::kLINEAR);
+    }
+    return false;
+}
+
+void EluPluginDynamic::configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs,
+    const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs)
+{
+    mType = in[0].desc.type;
+}
+
+size_t EluPluginDynamic::getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs,
+    const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const
+{
+    return 0;
+}
+int EluPluginDynamic::enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
+    const nvinfer1::PluginTensorDesc* outputDesc, const void* const* inputs, void* const* outputs, void* workspace,
+    cudaStream_t stream)
+{
+
+    const int inputVolume = samplesCommon::volume(inputDesc[0].dims);
+    int status = -1;
+
+    // Our plugin outputs only one tensor
+    // Launch CUDA kernel wrapper and save its return value
+    if (mType == DataType::kFLOAT)
+    {
+        const float* input = static_cast<const float*>(inputs[0]);
+        float* output = static_cast<float*>(outputs[0]);
+        status = computeElu(stream, inputVolume, input, output);
+    }
+    else if (mType == DataType::kHALF)
+    {
+        const half* input = static_cast<const half*>(inputs[0]);
+        half* output = static_cast<half*>(outputs[0]);
+        status = computeElu(stream, inputVolume, input, output);
+    }
+    else
+    {
+        assert(false);
+    }
+
+    return status;
+}
+
+// IPluginV2Ext Methods
+nvinfer1::DataType EluPluginDynamic::getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const
+{
+    assert(index == 0);
+    assert(inputTypes[0] == DataType::kFLOAT || inputTypes[0] == DataType::kHALF);
+    return inputTypes[0];
+}
+
+// IPluginV2 Methods
+
+const char* EluPluginDynamic::getPluginType() const
+{
+    return GELU_PLUGIN_NAME;
+}
+
+const char* EluPluginDynamic::getPluginVersion() const
+{
+    return GELU_PLUGIN_VERSION;
+}
+
+int EluPluginDynamic::getNbOutputs() const
+{
+    return 1;
+}
+
+int EluPluginDynamic::initialize()
+{
+    return 0;
+}
+
+void EluPluginDynamic::terminate() {}
+
+size_t EluPluginDynamic::getSerializationSize() const
+{
+    return sizeof(DataType);
+}
+
+void EluPluginDynamic::serialize(void* buffer) const
+{
+    char *d = static_cast<char*>(buffer), *a = d;
+    writeToBuffer(d, mType);
+    assert(d == a + getSerializationSize());
+}
+
+void EluPluginDynamic::destroy()
+{
+    // This gets called when the network containing plugin is destroyed
+    delete this;
+}
+
+void EluPluginDynamic::setPluginNamespace(const char* libNamespace)
+{
+    mNamespace = libNamespace;
+}
+
+const char* EluPluginDynamic::getPluginNamespace() const
+{
+    return mNamespace.c_str();
+}
+
+///////////////
+
+EluPluginDynamicCreator::EluPluginDynamicCreator()
+{
+
+    // Fill PluginFieldCollection with PluginField arguments metadata
+    mFC.nbFields = mPluginAttributes.size();
+    mFC.fields = mPluginAttributes.data();
+}
+
+const char* EluPluginDynamicCreator::getPluginName() const
+{
+    return GELU_PLUGIN_NAME;
+}
+
+const char* EluPluginDynamicCreator::getPluginVersion() const
+{
+    return GELU_PLUGIN_VERSION;
+}
+
+const PluginFieldCollection* EluPluginDynamicCreator::getFieldNames()
+{
+    return &mFC;
+}
+
+IPluginV2* EluPluginDynamicCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
+{
+    gLogVerbose << "Creating EluPluginDynamic...\n";
+    EluPluginDynamic* p = new EluPluginDynamic(name);
+    return p;
+}
+
+IPluginV2* EluPluginDynamicCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength)
+{
+    // This object will be deleted when the network is destroyed, which will
+    // call EluPluginDynamic::destroy()
+    return new EluPluginDynamic(name, serialData, serialLength);
+}
+
+void EluPluginDynamicCreator::setPluginNamespace(const char* libNamespace)
+{
+    mNamespace = libNamespace;
+}
+
+const char* EluPluginDynamicCreator::getPluginNamespace() const
+{
+    return mNamespace.c_str();
+}
+}
diff --git a/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h b/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h
new file mode 100644
index 00000000..f09ef9a1
--- /dev/null
+++ b/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h
@@ -0,0 +1,102 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef TRT_GELU_PLUGIN_H
+#define TRT_GELU_PLUGIN_H
+
+#include "NvInferPlugin.h"
+#include <string>
+#include <vector>
+
+namespace elu
+{
+
+// One of the preferred ways of making TensorRT to be able to see
+// our custom layer requires extending IPluginV2 and IPluginCreator classes.
+// For requirements for overriden functions, check TensorRT API docs.
+
+class EluPluginDynamic : public nvinfer1::IPluginV2DynamicExt
+{
+public:
+    EluPluginDynamic(const std::string name);
+
+    EluPluginDynamic(const std::string name, const void* data, size_t length);
+
+    // It doesn't make sense to make EluPluginDynamic without arguments, so we delete
+    // default constructor.
+    EluPluginDynamic() = delete;
+
+    // IPluginV2DynamicExt Methods
+    nvinfer1::IPluginV2DynamicExt* clone() const override;
+    nvinfer1::DimsExprs getOutputDimensions(
+        int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs, nvinfer1::IExprBuilder& exprBuilder) override;
+    bool supportsFormatCombination(
+        int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs) override;
+    void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs,
+        const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) override;
+    size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs,
+        const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const override;
+    int enqueue(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc,
+        const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) override;
+
+    // IPluginV2Ext Methods
+    nvinfer1::DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override;
+
+    // IPluginV2 Methods
+    const char* getPluginType() const override;
+    const char* getPluginVersion() const override;
+    int getNbOutputs() const override;
+    int initialize() override;
+    void terminate() override;
+    size_t getSerializationSize() const override;
+    void serialize(void* buffer) const override;
+    void destroy() override;
+    void setPluginNamespace(const char* pluginNamespace) override;
+    const char* getPluginNamespace() const override;
+
+private:
+    const std::string mLayerName;
+    std::string mNamespace;
+
+    nvinfer1::DataType mType;
+};
+
+class EluPluginDynamicCreator : public nvinfer1::IPluginCreator
+{
+public:
+    EluPluginDynamicCreator();
+
+    const char* getPluginName() const override;
+
+    const char* getPluginVersion() const override;
+
+    const nvinfer1::PluginFieldCollection* getFieldNames() override;
+
+    nvinfer1::IPluginV2* createPlugin(const char* name, const nvinfer1::PluginFieldCollection* fc) override;
+
+    nvinfer1::IPluginV2* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override;
+
+    void setPluginNamespace(const char* pluginNamespace) override;
+
+    const char* getPluginNamespace() const override;
+
+private:
+    static nvinfer1::PluginFieldCollection mFC;
+    static std::vector<nvinfer1::PluginField> mPluginAttributes;
+    std::string mNamespace;
+};
+}
+#endif // TRT_GELU_PLUGIN_H
diff --git a/notebooks/asian_barrier_option/elu_activation/plugins/pluginKernels.h b/notebooks/asian_barrier_option/elu_activation/plugins/pluginKernels.h
new file mode 100644
index 00000000..e9bd791a
--- /dev/null
+++ b/notebooks/asian_barrier_option/elu_activation/plugins/pluginKernels.h
@@ -0,0 +1,237 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef TRT_PLUGIN_KERNELS_H
+#define TRT_PLUGIN_KERNELS_H
+
+#include "NvInfer.h"
+#include <cassert>
+#include <cub/cub.cuh>
+#include <pluginUtil.h>
+#include <vector>
+
+namespace elu
+{
+
+template <typename T, unsigned TPB>
+__global__ void scaledSoftmaxKernelSmall(const int ld, const float rsqrtHeadSize, const T* input, T* output)
+{
+    scaledSoftmaxSmall<T, TPB>(ld, ld, rsqrtHeadSize, input, output);
+}
+
+template <typename T, unsigned TPB>
+__global__ void scaledSoftmaxKernel(const int ld, const float rsqrtHeadSize, const T* input, T* output)
+{
+    scaledSoftmax<T, TPB>(ld, ld, rsqrtHeadSize, input, output);
+}
+
+template <typename T>
+int computeScaledSoftmax(
+    cudaStream_t stream, const int ld, const int B, const int N, const float rsqrtHeadSize, const T* input, T* output)
+{
+
+    const dim3 grid(ld * N, B, 1);
+
+    if (ld <= 32)
+    {
+        const int blockSize = 32;
+        scaledSoftmaxKernelSmall<T, blockSize><<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, input, output);
+    }
+    else if (ld <= 128)
+    {
+        const int blockSize = 128;
+        scaledSoftmaxKernelSmall<T, blockSize><<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, input, output);
+    }
+    else if (ld == 384)
+    {
+        const int blockSize = 384;
+        scaledSoftmaxKernelSmall<T, blockSize><<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, input, output);
+    }
+    else
+    {
+        const int blockSize = 256;
+
+        scaledSoftmaxKernel<T, blockSize><<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, input, output);
+    }
+
+    CHECK(cudaPeekAtLastError());
+    return 0;
+}
+
+template <typename T, unsigned TPB>
+__global__ void maskedScaledSoftmaxKernelSmall(
+    const int ld, const float rsqrtHeadSize, const int* maskIdx, const T* input, T* output)
+{
+    __shared__ int lastValid;
+
+    if (threadIdx.x == 0)
+    {
+        lastValid = min(ld, maskIdx[blockIdx.y]);
+    }
+    __syncthreads();
+
+    scaledSoftmaxSmall<T, TPB>(ld, lastValid, rsqrtHeadSize, input, output);
+}
+
+template <typename T, unsigned TPB>
+__global__ void maskedScaledSoftmaxKernel(
+    const int ld, const float rsqrtHeadSize, const int* maskIdx, const T* input, T* output)
+{
+
+    __shared__ int lastValid;
+
+    if (threadIdx.x == 0)
+    {
+        lastValid = min(ld, maskIdx[blockIdx.y]);
+    }
+    __syncthreads();
+    scaledSoftmax<T, TPB>(ld, lastValid, rsqrtHeadSize, input, output);
+}
+
+template <typename T>
+int computeMaskedScaledSoftmax(cudaStream_t stream, const int ld, const int B, const int N, const float rsqrtHeadSize,
+    const int* maskIdx, const T* input, T* output)
+{
+    // Mask idx is of length B and assumes the valid region is contiguous starting
+    // from the beginning of the sequence
+
+    const dim3 grid(ld * N, B, 1);
+
+    if (ld <= 32)
+    {
+        const int blockSize = 32;
+        maskedScaledSoftmaxKernelSmall<T, blockSize>
+            <<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, maskIdx, input, output);
+    }
+    else if (ld <= 128)
+    {
+        const int blockSize = 128;
+        maskedScaledSoftmaxKernelSmall<T, blockSize>
+            <<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, maskIdx, input, output);
+    }
+    else if (ld == 384)
+    {
+        const int blockSize = 384;
+        maskedScaledSoftmaxKernelSmall<T, blockSize>
+            <<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, maskIdx, input, output);
+    }
+    else
+    {
+        const int blockSize = 256;
+
+        maskedScaledSoftmaxKernel<T, blockSize>
+            <<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, maskIdx, input, output);
+    }
+
+    CHECK(cudaPeekAtLastError());
+    return 0;
+}
+
+template <unsigned TPB>
+__global__ void maskIdxKernelSmall(int ld, const int* mask, int* maskIdx)
+{
+
+    using BlockReduce = cub::BlockReduce<int, TPB>;
+    __shared__ typename BlockReduce::TempStorage tmpStorage;
+
+    // ld is S
+    // blockIdx.x is b
+
+    const int offset = blockIdx.x * ld; // batch strides of S
+
+    cub::Min min;
+    int threadData(ld); // if the mask admits all values
+
+    const int idx = offset + threadIdx.x;
+    if (threadIdx.x < ld)
+    {
+        const int val = mask[idx];
+        if (val == 0) // masked position: report thread idx
+        {
+            threadData = threadIdx.x;
+        }
+    }
+
+    const auto minIdx = BlockReduce(tmpStorage).Reduce(threadData, min);
+
+    if (threadIdx.x == 0)
+    {
+        maskIdx[blockIdx.x] = minIdx;
+    }
+}
+
+template <unsigned TPB>
+__global__ void maskIdxKernel(int ld, const int* mask, int* maskIdx)
+{
+
+    using BlockReduce = cub::BlockReduce<int, TPB>;
+    __shared__ typename BlockReduce::TempStorage tmpStorage;
+
+    // ld is S
+    // blockIdx.x is b
+
+    const int offset = blockIdx.x * ld; // batch strides of S
+
+    cub::Min min;
+    int threadData(ld); // if the mask admits all values
+
+    for (int i = threadIdx.x; i < ld; i += TPB)
+    {
+        const int idx = offset + i;
+        const int val = mask[idx];
+        if (val == 0) // masked position: report thread idx
+        {
+            threadData = min(threadData, i);
+        }
+    }
+
+    const auto minIdx = BlockReduce(tmpStorage).Reduce(threadData, min);
+
+    if (threadIdx.x == 0)
+    {
+        maskIdx[blockIdx.x] = minIdx;
+    }
+}
+
+inline int computeMaskIdx(cudaStream_t stream, const int S, const int B, const int* mask, int* maskIdx)
+{
+    // Mask idx is of length B and assumes the valid region is contiguous starting
+    // from the beginning of the sequence
+
+    // Assume n = BxS
+    if (S <= 32)
+    {
+        maskIdxKernelSmall<32><<<B, 32, 0, stream>>>(S, mask, maskIdx);
+    }
+    else if (S <= 128)
+    {
+        maskIdxKernelSmall<128><<<B, 128, 0, stream>>>(S, mask, maskIdx);
+    }
+    else if (S == 384)
+    {
+        maskIdxKernelSmall<384><<<B, 384, 0, stream>>>(S, mask, maskIdx);
+    }
+    else
+    {
+        maskIdxKernel<256><<<B, 256, 0, stream>>>(S, mask, maskIdx);
+    }
+
+    CHECK(cudaPeekAtLastError());
+
+    return 0;
+}
+}
+#endif // TRT_PLUGIN_KERNELS_H
diff --git a/notebooks/asian_barrier_option/elu_activation/plugins/pluginUtil.h b/notebooks/asian_barrier_option/elu_activation/plugins/pluginUtil.h
new file mode 100644
index 00000000..bc9337b5
--- /dev/null
+++ b/notebooks/asian_barrier_option/elu_activation/plugins/pluginUtil.h
@@ -0,0 +1,375 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef TRT_PLUGIN_UTIL_H
+#define TRT_PLUGIN_UTIL_H
+
+#include "cublas_v2.h"
+#include "cuda_fp16.h"
+#include "common.h"
+#include <cub/cub.cuh>
+
+namespace elu
+{
+
+constexpr uint32_t BDIM = 0; // batch dimension
+constexpr uint32_t SDIM = 1; // seq len dimension
+constexpr uint32_t HDIM = 2; // hidden dimension
+
+#define DESER(d, m) m = readFromBuffer<decltype(m)>(d)
+
+#define HDI inline __host__ __device__
+
+// Helper function for serializing plugin
+template <typename T>
+inline void writeToBuffer(char*& buffer, const T& val)
+{
+    *reinterpret_cast<T*>(buffer) = val;
+    buffer += sizeof(T);
+}
+
+// Helper function for deserializing plugin
+template <typename T>
+inline T readFromBuffer(const char*& buffer)
+{
+    T val = *reinterpret_cast<const T*>(buffer);
+    buffer += sizeof(T);
+    return val;
+}
+
+template <typename T>
+inline T* deserToDev(const char*& buffer, size_t nbElem)
+{
+    T* dev = nullptr;
+    const size_t len = sizeof(T) * nbElem;
+    CHECK(cudaMalloc(&dev, len));
+    CHECK(cudaMemcpy(dev, buffer, len, cudaMemcpyHostToDevice));
+
+    buffer += len;
+    return dev;
+}
+
+template <typename T>
+inline void serFromDev(char*& buffer, const T* data, size_t nbElem)
+{
+    const size_t len = sizeof(T) * nbElem;
+    CHECK(cudaMemcpy(buffer, data, len, cudaMemcpyDeviceToHost));
+    buffer += len;
+}
+
+template <typename T>
+__device__ inline T rsqrt(const T& x);
+
+template <>
+__device__ inline float rsqrt(const float& x)
+{
+    return rsqrtf(x);
+}
+
+template <>
+__device__ inline half rsqrt(const half& x)
+{
+    return hrsqrt(x);
+}
+
+template <typename T>
+__device__ inline T tanh(const T& x);
+
+template <>
+__device__ inline float tanh(const float& x)
+{
+    return tanhf(x);
+}
+
+template <>
+__device__ inline half tanh(const half& x)
+{
+    const float tmp = tanhf(__half2float(x));
+    return __float2half(tmp);
+}
+
+template <>
+__device__ inline half2 tanh(const half2& x)
+{
+    // at the moment, there is no half2 tanh builtin
+    float2 tmp = (__half22float2(x));
+    tmp.x = tanhf(tmp.x);
+    tmp.y = tanhf(tmp.y);
+    return __float22half2_rn(tmp);
+}
+
+template <typename T>
+__device__ inline T exp(const T& x);
+
+template <>
+__device__ inline float exp(const float& x)
+{
+    return expf(x);
+}
+
+template <>
+__device__ inline half exp(const half& x)
+{
+    return hexp(x);
+}
+
+template <>
+__device__ inline half2 exp(const half2& x)
+{
+    return h2exp(x);
+}
+
+using kv_float = cub::KeyValuePair<float, float>;
+using kv_half = cub::KeyValuePair<half, half>;
+using kv_half2 = cub::KeyValuePair<half2, half2>;
+
+__device__ inline kv_float operator+(const kv_float& a, const kv_float& b)
+{
+    return kv_float(a.key + b.key, a.value + b.value);
+}
+
+__device__ inline kv_half operator+(const kv_half& a, const kv_half& b)
+{
+    const half2 a2 = __halves2half2(a.key, a.value);
+    const half2 b2 = __halves2half2(b.key, b.value);
+    const half2 res = __hadd2(a2, b2);
+    return kv_half(res.x, res.y);
+}
+
+__device__ inline kv_half2 operator+(const kv_half2& a, const kv_half2& b)
+{
+    return kv_half2(__hadd2(a.key, b.key), __hadd2(a.value, b.value));
+}
+
+template <typename T>
+using kvp = cub::KeyValuePair<T, T>;
+
+template <typename T, int TPB>
+__device__ inline void layerNorm(
+    const kvp<T>& threadData, const int ld, const int offset, const float* beta, const float* gamma, T* output)
+{
+    // Assuming threadData is already divided by ld
+
+    using BlockReduce = cub::BlockReduce<kvp<T>, TPB>;
+    __shared__ typename BlockReduce::TempStorage temp_storage;
+    __shared__ T mu;     // mean
+    __shared__ T rsigma; // 1 / std.dev.
+
+    const auto sumKV = BlockReduce(temp_storage).Reduce(threadData, cub::Sum());
+
+    if (threadIdx.x == 0)
+    {
+        mu = sumKV.key;
+        rsigma = rsqrt(sumKV.value - mu * mu);
+    }
+    __syncthreads();
+
+    for (int i = threadIdx.x; i < ld; i += TPB)
+    {
+        const int idx = offset + i;
+        const T val = output[idx];
+        const T g(gamma[i]);
+        const T b(beta[i]);
+        output[idx] = g * (val - mu) * rsigma + b;
+    }
+}
+
+template <typename T, int TPB>
+__device__ inline void layerNormSmall(const T val, const kvp<T>& threadData, const int ld, const int idx,
+    const float* beta, const float* gamma, T* output)
+{
+    // Assuming threadData is already divided by ld
+    // Small settings: the block covers the leading dimension TPB >= ld. The input
+    // value is available in a register
+
+    using BlockReduce = cub::BlockReduce<kvp<T>, TPB>;
+    __shared__ typename BlockReduce::TempStorage temp_storage;
+    __shared__ T mu;     // mean
+    __shared__ T rsigma; // 1 / std.dev.
+
+    const auto sumKV = BlockReduce(temp_storage).Reduce(threadData, cub::Sum());
+
+    if (threadIdx.x == 0)
+    {
+        mu = sumKV.key;
+        rsigma = rsqrt(sumKV.value - mu * mu);
+    }
+    __syncthreads();
+
+    if (threadIdx.x < ld)
+    {
+        const T g(gamma[threadIdx.x]);
+        const T b(beta[threadIdx.x]);
+        output[idx] = g * (val - mu) * rsigma + b;
+    }
+}
+
+template <typename T, unsigned TPB>
+__device__ inline void scaledSoftmaxSmall(
+    const int ld, const int lastValid, const float rsqrtHeadSize, const T* input, T* output)
+{
+
+    using BlockReduce = cub::BlockReduce<float, TPB>;
+
+    __shared__ typename BlockReduce::TempStorage tmpStorage;
+
+    __shared__ float rZ;
+
+    const int offset = (blockIdx.y * gridDim.x + blockIdx.x) * ld;
+
+    const float w(rsqrtHeadSize);
+    cub::Sum sum;
+    float threadData(0);
+
+    const int idx = offset + threadIdx.x;
+    if (threadIdx.x < lastValid)
+    {
+        const float val = input[idx];
+        threadData = exp(val * w);
+    }
+
+    const auto Z = BlockReduce(tmpStorage).Reduce(threadData, sum);
+
+    if (threadIdx.x == 0)
+    {
+        rZ = (1.f) / Z;
+    }
+    __syncthreads();
+
+    if (threadIdx.x < ld)
+    {
+        // this will be 0 for threadIdx.x >= lastValid
+        output[idx] = T(threadData * rZ);
+    }
+}
+
+template <typename T, unsigned TPB>
+__device__ inline void scaledSoftmax(
+    const int ld, const int lastValid, const float rsqrtHeadSize, const T* input, T* output)
+{
+
+    using BlockReduce = cub::BlockReduce<float, TPB>;
+    __shared__ typename BlockReduce::TempStorage tmpStorage;
+
+    __shared__ float rZ;
+
+    const int offset = (blockIdx.y * gridDim.x + blockIdx.x) * ld;
+
+    const float w(rsqrtHeadSize);
+    cub::Sum sum;
+    float threadData(0);
+
+    for (int i = threadIdx.x; i < lastValid; i += TPB)
+    {
+        const int idx = offset + i;
+        const float val = input[idx];
+        threadData += exp(val * w);
+    }
+
+    const auto Z = BlockReduce(tmpStorage).Reduce(threadData, sum);
+
+    if (threadIdx.x == 0)
+    {
+        rZ = 1.f / Z;
+    }
+    __syncthreads();
+
+    for (int i = threadIdx.x; i < ld; i += TPB)
+    {
+        const int idx = offset + i;
+        const float val = (i < lastValid) ? exp(float(input[idx]) * w) * rZ : 0.f;
+        output[idx] = T(val);
+    }
+}
+
+template <typename IntType>
+constexpr HDI IntType ceildiv(IntType a, IntType b)
+{
+    return (a + b - 1) / b;
+}
+template <typename IntType>
+constexpr HDI IntType alignTo(IntType a, IntType b)
+{
+    return ceildiv(a, b) * b;
+}
+
+template <typename T>
+cublasStatus_t inline cublasGemm(cublasHandle_t handle, cublasOperation_t transa, cublasOperation_t transb, int m,
+    int n, int k, const T alpha, const T* A, int lda, const T* B, int ldb, const T beta, T* C, int ldc);
+
+template <>
+cublasStatus_t inline cublasGemm(cublasHandle_t handle, cublasOperation_t transa, cublasOperation_t transb, int m,
+    int n, int k, const float alpha, const float* A, int lda, const float* B, int ldb, const float beta, float* C,
+    int ldc)
+{
+
+    return cublasSgemm(handle, transa, transb, m, n, k, &alpha, A, lda, B, ldb, &beta, C, ldc);
+}
+
+template <>
+cublasStatus_t inline cublasGemm(cublasHandle_t handle, cublasOperation_t transa, cublasOperation_t transb, int m,
+    int n, int k, const half alpha, const half* A, int lda, const half* B, int ldb, const half beta, half* C, int ldc)
+{
+    return cublasHgemm(handle, transa, transb, m, n, k, &alpha, A, lda, B, ldb, &beta, C, ldc);
+}
+
+template <typename T>
+cublasStatus_t inline cublasGemmStridedBatched(cublasHandle_t handle, cublasOperation_t transa,
+    cublasOperation_t transb, int m, int n, int k, const T alpha, const T* A, int lda, long long int strideA,
+    const T* B, int ldb, long long int strideB, const T beta, T* C, int ldc, long long int strideC, int batchCount);
+
+template <>
+cublasStatus_t inline cublasGemmStridedBatched(cublasHandle_t handle, cublasOperation_t transa,
+    cublasOperation_t transb, int m, int n, int k, const float alpha, const float* A, int lda, long long int strideA,
+    const float* B, int ldb, long long int strideB, const float beta, float* C, int ldc, long long int strideC,
+    int batchCount)
+{
+
+    return cublasSgemmStridedBatched(
+        handle, transa, transb, m, n, k, &alpha, A, lda, strideA, B, ldb, strideB, &beta, C, ldc, strideC, batchCount);
+}
+
+template <>
+cublasStatus_t inline cublasGemmStridedBatched(cublasHandle_t handle, cublasOperation_t transa,
+    cublasOperation_t transb, int m, int n, int k, const half alpha, const half* A, int lda, long long int strideA,
+    const half* B, int ldb, long long int strideB, const half beta, half* C, int ldc, long long int strideC,
+    int batchCount)
+{
+    return cublasHgemmStridedBatched(
+        handle, transa, transb, m, n, k, &alpha, A, lda, strideA, B, ldb, strideB, &beta, C, ldc, strideC, batchCount);
+}
+
+struct CublasConfigHelper
+{
+    cublasPointerMode_t pm;
+    cublasMath_t mm;
+    cublasHandle_t cublas;
+    CublasConfigHelper(cublasHandle_t cublas_)
+        : cublas(cublas_)
+    {
+        cublasGetPointerMode(cublas, &pm);
+        cublasGetMathMode(cublas, &mm);
+        cublasSetPointerMode(cublas, CUBLAS_POINTER_MODE_HOST);
+        cublasSetMathMode(cublas, CUBLAS_TENSOR_OP_MATH);
+    }
+    ~CublasConfigHelper()
+    {
+        cublasSetMathMode(cublas, mm);
+        cublasSetPointerMode(cublas, pm);
+    }
+};
+}
+#endif // TRT_PLUGIN_UTIL_H
diff --git a/notebooks/asian_barrier_option/helper_cuda.h b/notebooks/asian_barrier_option/helper_cuda.h
new file mode 100644
index 00000000..d44d4cd2
--- /dev/null
+++ b/notebooks/asian_barrier_option/helper_cuda.h
@@ -0,0 +1,898 @@
+/**
+ * Copyright 1993-2017 NVIDIA Corporation.  All rights reserved.
+ *
+ * Please refer to the NVIDIA end user license agreement (EULA) associated
+ * with this source code for terms and conditions that govern your use of
+ * this software. Any use, reproduction, disclosure, or distribution of
+ * this software and related documentation outside the terms of the EULA
+ * is strictly prohibited.
+ *
+ */
+
+////////////////////////////////////////////////////////////////////////////////
+// These are CUDA Helper functions for initialization and error checking
+
+#ifndef COMMON_HELPER_CUDA_H_
+#define COMMON_HELPER_CUDA_H_
+
+#pragma once
+
+#include <stdint.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#include <helper_string.h>
+
+#ifndef EXIT_WAIVED
+#define EXIT_WAIVED 2
+#endif
+
+// Note, it is required that your SDK sample to include the proper header
+// files, please refer the CUDA examples for examples of the needed CUDA
+// headers, which may change depending on which CUDA functions are used.
+
+// CUDA Runtime error messages
+#ifdef __DRIVER_TYPES_H__
+static const char *_cudaGetErrorEnum(cudaError_t error) {
+  return cudaGetErrorName(error);
+}
+#endif
+
+#ifdef CUDA_DRIVER_API
+// CUDA Driver API errors
+static const char *_cudaGetErrorEnum(CUresult error) {
+  static char unknown[] = "<unknown>";
+  const char *ret = NULL;
+  cuGetErrorName(error, &ret);
+  return ret ? ret : unknown;
+}
+#endif
+
+#ifdef CUBLAS_API_H_
+// cuBLAS API errors
+static const char *_cudaGetErrorEnum(cublasStatus_t error) {
+  switch (error) {
+    case CUBLAS_STATUS_SUCCESS:
+      return "CUBLAS_STATUS_SUCCESS";
+
+    case CUBLAS_STATUS_NOT_INITIALIZED:
+      return "CUBLAS_STATUS_NOT_INITIALIZED";
+
+    case CUBLAS_STATUS_ALLOC_FAILED:
+      return "CUBLAS_STATUS_ALLOC_FAILED";
+
+    case CUBLAS_STATUS_INVALID_VALUE:
+      return "CUBLAS_STATUS_INVALID_VALUE";
+
+    case CUBLAS_STATUS_ARCH_MISMATCH:
+      return "CUBLAS_STATUS_ARCH_MISMATCH";
+
+    case CUBLAS_STATUS_MAPPING_ERROR:
+      return "CUBLAS_STATUS_MAPPING_ERROR";
+
+    case CUBLAS_STATUS_EXECUTION_FAILED:
+      return "CUBLAS_STATUS_EXECUTION_FAILED";
+
+    case CUBLAS_STATUS_INTERNAL_ERROR:
+      return "CUBLAS_STATUS_INTERNAL_ERROR";
+
+    case CUBLAS_STATUS_NOT_SUPPORTED:
+      return "CUBLAS_STATUS_NOT_SUPPORTED";
+
+    case CUBLAS_STATUS_LICENSE_ERROR:
+      return "CUBLAS_STATUS_LICENSE_ERROR";
+  }
+
+  return "<unknown>";
+}
+#endif
+
+#ifdef _CUFFT_H_
+// cuFFT API errors
+static const char *_cudaGetErrorEnum(cufftResult error) {
+  switch (error) {
+    case CUFFT_SUCCESS:
+      return "CUFFT_SUCCESS";
+
+    case CUFFT_INVALID_PLAN:
+      return "CUFFT_INVALID_PLAN";
+
+    case CUFFT_ALLOC_FAILED:
+      return "CUFFT_ALLOC_FAILED";
+
+    case CUFFT_INVALID_TYPE:
+      return "CUFFT_INVALID_TYPE";
+
+    case CUFFT_INVALID_VALUE:
+      return "CUFFT_INVALID_VALUE";
+
+    case CUFFT_INTERNAL_ERROR:
+      return "CUFFT_INTERNAL_ERROR";
+
+    case CUFFT_EXEC_FAILED:
+      return "CUFFT_EXEC_FAILED";
+
+    case CUFFT_SETUP_FAILED:
+      return "CUFFT_SETUP_FAILED";
+
+    case CUFFT_INVALID_SIZE:
+      return "CUFFT_INVALID_SIZE";
+
+    case CUFFT_UNALIGNED_DATA:
+      return "CUFFT_UNALIGNED_DATA";
+
+    case CUFFT_INCOMPLETE_PARAMETER_LIST:
+      return "CUFFT_INCOMPLETE_PARAMETER_LIST";
+
+    case CUFFT_INVALID_DEVICE:
+      return "CUFFT_INVALID_DEVICE";
+
+    case CUFFT_PARSE_ERROR:
+      return "CUFFT_PARSE_ERROR";
+
+    case CUFFT_NO_WORKSPACE:
+      return "CUFFT_NO_WORKSPACE";
+
+    case CUFFT_NOT_IMPLEMENTED:
+      return "CUFFT_NOT_IMPLEMENTED";
+
+    case CUFFT_LICENSE_ERROR:
+      return "CUFFT_LICENSE_ERROR";
+
+    case CUFFT_NOT_SUPPORTED:
+      return "CUFFT_NOT_SUPPORTED";
+  }
+
+  return "<unknown>";
+}
+#endif
+
+#ifdef CUSPARSEAPI
+// cuSPARSE API errors
+static const char *_cudaGetErrorEnum(cusparseStatus_t error) {
+  switch (error) {
+    case CUSPARSE_STATUS_SUCCESS:
+      return "CUSPARSE_STATUS_SUCCESS";
+
+    case CUSPARSE_STATUS_NOT_INITIALIZED:
+      return "CUSPARSE_STATUS_NOT_INITIALIZED";
+
+    case CUSPARSE_STATUS_ALLOC_FAILED:
+      return "CUSPARSE_STATUS_ALLOC_FAILED";
+
+    case CUSPARSE_STATUS_INVALID_VALUE:
+      return "CUSPARSE_STATUS_INVALID_VALUE";
+
+    case CUSPARSE_STATUS_ARCH_MISMATCH:
+      return "CUSPARSE_STATUS_ARCH_MISMATCH";
+
+    case CUSPARSE_STATUS_MAPPING_ERROR:
+      return "CUSPARSE_STATUS_MAPPING_ERROR";
+
+    case CUSPARSE_STATUS_EXECUTION_FAILED:
+      return "CUSPARSE_STATUS_EXECUTION_FAILED";
+
+    case CUSPARSE_STATUS_INTERNAL_ERROR:
+      return "CUSPARSE_STATUS_INTERNAL_ERROR";
+
+    case CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED:
+      return "CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED";
+  }
+
+  return "<unknown>";
+}
+#endif
+
+#ifdef CUSOLVER_COMMON_H_
+// cuSOLVER API errors
+static const char *_cudaGetErrorEnum(cusolverStatus_t error) {
+  switch (error) {
+    case CUSOLVER_STATUS_SUCCESS:
+      return "CUSOLVER_STATUS_SUCCESS";
+    case CUSOLVER_STATUS_NOT_INITIALIZED:
+      return "CUSOLVER_STATUS_NOT_INITIALIZED";
+    case CUSOLVER_STATUS_ALLOC_FAILED:
+      return "CUSOLVER_STATUS_ALLOC_FAILED";
+    case CUSOLVER_STATUS_INVALID_VALUE:
+      return "CUSOLVER_STATUS_INVALID_VALUE";
+    case CUSOLVER_STATUS_ARCH_MISMATCH:
+      return "CUSOLVER_STATUS_ARCH_MISMATCH";
+    case CUSOLVER_STATUS_MAPPING_ERROR:
+      return "CUSOLVER_STATUS_MAPPING_ERROR";
+    case CUSOLVER_STATUS_EXECUTION_FAILED:
+      return "CUSOLVER_STATUS_EXECUTION_FAILED";
+    case CUSOLVER_STATUS_INTERNAL_ERROR:
+      return "CUSOLVER_STATUS_INTERNAL_ERROR";
+    case CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED:
+      return "CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED";
+    case CUSOLVER_STATUS_NOT_SUPPORTED:
+      return "CUSOLVER_STATUS_NOT_SUPPORTED ";
+    case CUSOLVER_STATUS_ZERO_PIVOT:
+      return "CUSOLVER_STATUS_ZERO_PIVOT";
+    case CUSOLVER_STATUS_INVALID_LICENSE:
+      return "CUSOLVER_STATUS_INVALID_LICENSE";
+  }
+
+  return "<unknown>";
+}
+#endif
+
+#ifdef CURAND_H_
+// cuRAND API errors
+static const char *_cudaGetErrorEnum(curandStatus_t error) {
+  switch (error) {
+    case CURAND_STATUS_SUCCESS:
+      return "CURAND_STATUS_SUCCESS";
+
+    case CURAND_STATUS_VERSION_MISMATCH:
+      return "CURAND_STATUS_VERSION_MISMATCH";
+
+    case CURAND_STATUS_NOT_INITIALIZED:
+      return "CURAND_STATUS_NOT_INITIALIZED";
+
+    case CURAND_STATUS_ALLOCATION_FAILED:
+      return "CURAND_STATUS_ALLOCATION_FAILED";
+
+    case CURAND_STATUS_TYPE_ERROR:
+      return "CURAND_STATUS_TYPE_ERROR";
+
+    case CURAND_STATUS_OUT_OF_RANGE:
+      return "CURAND_STATUS_OUT_OF_RANGE";
+
+    case CURAND_STATUS_LENGTH_NOT_MULTIPLE:
+      return "CURAND_STATUS_LENGTH_NOT_MULTIPLE";
+
+    case CURAND_STATUS_DOUBLE_PRECISION_REQUIRED:
+      return "CURAND_STATUS_DOUBLE_PRECISION_REQUIRED";
+
+    case CURAND_STATUS_LAUNCH_FAILURE:
+      return "CURAND_STATUS_LAUNCH_FAILURE";
+
+    case CURAND_STATUS_PREEXISTING_FAILURE:
+      return "CURAND_STATUS_PREEXISTING_FAILURE";
+
+    case CURAND_STATUS_INITIALIZATION_FAILED:
+      return "CURAND_STATUS_INITIALIZATION_FAILED";
+
+    case CURAND_STATUS_ARCH_MISMATCH:
+      return "CURAND_STATUS_ARCH_MISMATCH";
+
+    case CURAND_STATUS_INTERNAL_ERROR:
+      return "CURAND_STATUS_INTERNAL_ERROR";
+  }
+
+  return "<unknown>";
+}
+#endif
+
+#ifdef NVJPEGAPI
+// nvJPEG API errors
+static const char *_cudaGetErrorEnum(nvjpegStatus_t error) {
+  switch (error) {
+    case NVJPEG_STATUS_SUCCESS:
+      return "NVJPEG_STATUS_SUCCESS";
+
+    case NVJPEG_STATUS_NOT_INITIALIZED:
+      return "NVJPEG_STATUS_NOT_INITIALIZED";
+
+    case NVJPEG_STATUS_INVALID_PARAMETER:
+      return "NVJPEG_STATUS_INVALID_PARAMETER";
+
+    case NVJPEG_STATUS_BAD_JPEG:
+      return "NVJPEG_STATUS_BAD_JPEG";
+
+    case NVJPEG_STATUS_JPEG_NOT_SUPPORTED:
+      return "NVJPEG_STATUS_JPEG_NOT_SUPPORTED";
+
+    case NVJPEG_STATUS_ALLOCATOR_FAILURE:
+      return "NVJPEG_STATUS_ALLOCATOR_FAILURE";
+
+    case NVJPEG_STATUS_EXECUTION_FAILED:
+      return "NVJPEG_STATUS_EXECUTION_FAILED";
+
+    case NVJPEG_STATUS_ARCH_MISMATCH:
+      return "NVJPEG_STATUS_ARCH_MISMATCH";
+
+    case NVJPEG_STATUS_INTERNAL_ERROR:
+      return "NVJPEG_STATUS_INTERNAL_ERROR";
+  }
+
+  return "<unknown>";
+}
+#endif
+
+#ifdef NV_NPPIDEFS_H
+// NPP API errors
+static const char *_cudaGetErrorEnum(NppStatus error) {
+  switch (error) {
+    case NPP_NOT_SUPPORTED_MODE_ERROR:
+      return "NPP_NOT_SUPPORTED_MODE_ERROR";
+
+    case NPP_ROUND_MODE_NOT_SUPPORTED_ERROR:
+      return "NPP_ROUND_MODE_NOT_SUPPORTED_ERROR";
+
+    case NPP_RESIZE_NO_OPERATION_ERROR:
+      return "NPP_RESIZE_NO_OPERATION_ERROR";
+
+    case NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY:
+      return "NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY";
+
+#if ((NPP_VERSION_MAJOR << 12) + (NPP_VERSION_MINOR << 4)) <= 0x5000
+
+    case NPP_BAD_ARG_ERROR:
+      return "NPP_BAD_ARGUMENT_ERROR";
+
+    case NPP_COEFF_ERROR:
+      return "NPP_COEFFICIENT_ERROR";
+
+    case NPP_RECT_ERROR:
+      return "NPP_RECTANGLE_ERROR";
+
+    case NPP_QUAD_ERROR:
+      return "NPP_QUADRANGLE_ERROR";
+
+    case NPP_MEM_ALLOC_ERR:
+      return "NPP_MEMORY_ALLOCATION_ERROR";
+
+    case NPP_HISTO_NUMBER_OF_LEVELS_ERROR:
+      return "NPP_HISTOGRAM_NUMBER_OF_LEVELS_ERROR";
+
+    case NPP_INVALID_INPUT:
+      return "NPP_INVALID_INPUT";
+
+    case NPP_POINTER_ERROR:
+      return "NPP_POINTER_ERROR";
+
+    case NPP_WARNING:
+      return "NPP_WARNING";
+
+    case NPP_ODD_ROI_WARNING:
+      return "NPP_ODD_ROI_WARNING";
+#else
+
+    // These are for CUDA 5.5 or higher
+    case NPP_BAD_ARGUMENT_ERROR:
+      return "NPP_BAD_ARGUMENT_ERROR";
+
+    case NPP_COEFFICIENT_ERROR:
+      return "NPP_COEFFICIENT_ERROR";
+
+    case NPP_RECTANGLE_ERROR:
+      return "NPP_RECTANGLE_ERROR";
+
+    case NPP_QUADRANGLE_ERROR:
+      return "NPP_QUADRANGLE_ERROR";
+
+    case NPP_MEMORY_ALLOCATION_ERR:
+      return "NPP_MEMORY_ALLOCATION_ERROR";
+
+    case NPP_HISTOGRAM_NUMBER_OF_LEVELS_ERROR:
+      return "NPP_HISTOGRAM_NUMBER_OF_LEVELS_ERROR";
+
+    case NPP_INVALID_HOST_POINTER_ERROR:
+      return "NPP_INVALID_HOST_POINTER_ERROR";
+
+    case NPP_INVALID_DEVICE_POINTER_ERROR:
+      return "NPP_INVALID_DEVICE_POINTER_ERROR";
+#endif
+
+    case NPP_LUT_NUMBER_OF_LEVELS_ERROR:
+      return "NPP_LUT_NUMBER_OF_LEVELS_ERROR";
+
+    case NPP_TEXTURE_BIND_ERROR:
+      return "NPP_TEXTURE_BIND_ERROR";
+
+    case NPP_WRONG_INTERSECTION_ROI_ERROR:
+      return "NPP_WRONG_INTERSECTION_ROI_ERROR";
+
+    case NPP_NOT_EVEN_STEP_ERROR:
+      return "NPP_NOT_EVEN_STEP_ERROR";
+
+    case NPP_INTERPOLATION_ERROR:
+      return "NPP_INTERPOLATION_ERROR";
+
+    case NPP_RESIZE_FACTOR_ERROR:
+      return "NPP_RESIZE_FACTOR_ERROR";
+
+    case NPP_HAAR_CLASSIFIER_PIXEL_MATCH_ERROR:
+      return "NPP_HAAR_CLASSIFIER_PIXEL_MATCH_ERROR";
+
+#if ((NPP_VERSION_MAJOR << 12) + (NPP_VERSION_MINOR << 4)) <= 0x5000
+
+    case NPP_MEMFREE_ERR:
+      return "NPP_MEMFREE_ERR";
+
+    case NPP_MEMSET_ERR:
+      return "NPP_MEMSET_ERR";
+
+    case NPP_MEMCPY_ERR:
+      return "NPP_MEMCPY_ERROR";
+
+    case NPP_MIRROR_FLIP_ERR:
+      return "NPP_MIRROR_FLIP_ERR";
+#else
+
+    case NPP_MEMFREE_ERROR:
+      return "NPP_MEMFREE_ERROR";
+
+    case NPP_MEMSET_ERROR:
+      return "NPP_MEMSET_ERROR";
+
+    case NPP_MEMCPY_ERROR:
+      return "NPP_MEMCPY_ERROR";
+
+    case NPP_MIRROR_FLIP_ERROR:
+      return "NPP_MIRROR_FLIP_ERROR";
+#endif
+
+    case NPP_ALIGNMENT_ERROR:
+      return "NPP_ALIGNMENT_ERROR";
+
+    case NPP_STEP_ERROR:
+      return "NPP_STEP_ERROR";
+
+    case NPP_SIZE_ERROR:
+      return "NPP_SIZE_ERROR";
+
+    case NPP_NULL_POINTER_ERROR:
+      return "NPP_NULL_POINTER_ERROR";
+
+    case NPP_CUDA_KERNEL_EXECUTION_ERROR:
+      return "NPP_CUDA_KERNEL_EXECUTION_ERROR";
+
+    case NPP_NOT_IMPLEMENTED_ERROR:
+      return "NPP_NOT_IMPLEMENTED_ERROR";
+
+    case NPP_ERROR:
+      return "NPP_ERROR";
+
+    case NPP_SUCCESS:
+      return "NPP_SUCCESS";
+
+    case NPP_WRONG_INTERSECTION_QUAD_WARNING:
+      return "NPP_WRONG_INTERSECTION_QUAD_WARNING";
+
+    case NPP_MISALIGNED_DST_ROI_WARNING:
+      return "NPP_MISALIGNED_DST_ROI_WARNING";
+
+    case NPP_AFFINE_QUAD_INCORRECT_WARNING:
+      return "NPP_AFFINE_QUAD_INCORRECT_WARNING";
+
+    case NPP_DOUBLE_SIZE_WARNING:
+      return "NPP_DOUBLE_SIZE_WARNING";
+
+    case NPP_WRONG_INTERSECTION_ROI_WARNING:
+      return "NPP_WRONG_INTERSECTION_ROI_WARNING";
+
+#if ((NPP_VERSION_MAJOR << 12) + (NPP_VERSION_MINOR << 4)) >= 0x6000
+    /* These are 6.0 or higher */
+    case NPP_LUT_PALETTE_BITSIZE_ERROR:
+      return "NPP_LUT_PALETTE_BITSIZE_ERROR";
+
+    case NPP_ZC_MODE_NOT_SUPPORTED_ERROR:
+      return "NPP_ZC_MODE_NOT_SUPPORTED_ERROR";
+
+    case NPP_QUALITY_INDEX_ERROR:
+      return "NPP_QUALITY_INDEX_ERROR";
+
+    case NPP_CHANNEL_ORDER_ERROR:
+      return "NPP_CHANNEL_ORDER_ERROR";
+
+    case NPP_ZERO_MASK_VALUE_ERROR:
+      return "NPP_ZERO_MASK_VALUE_ERROR";
+
+    case NPP_NUMBER_OF_CHANNELS_ERROR:
+      return "NPP_NUMBER_OF_CHANNELS_ERROR";
+
+    case NPP_COI_ERROR:
+      return "NPP_COI_ERROR";
+
+    case NPP_DIVISOR_ERROR:
+      return "NPP_DIVISOR_ERROR";
+
+    case NPP_CHANNEL_ERROR:
+      return "NPP_CHANNEL_ERROR";
+
+    case NPP_STRIDE_ERROR:
+      return "NPP_STRIDE_ERROR";
+
+    case NPP_ANCHOR_ERROR:
+      return "NPP_ANCHOR_ERROR";
+
+    case NPP_MASK_SIZE_ERROR:
+      return "NPP_MASK_SIZE_ERROR";
+
+    case NPP_MOMENT_00_ZERO_ERROR:
+      return "NPP_MOMENT_00_ZERO_ERROR";
+
+    case NPP_THRESHOLD_NEGATIVE_LEVEL_ERROR:
+      return "NPP_THRESHOLD_NEGATIVE_LEVEL_ERROR";
+
+    case NPP_THRESHOLD_ERROR:
+      return "NPP_THRESHOLD_ERROR";
+
+    case NPP_CONTEXT_MATCH_ERROR:
+      return "NPP_CONTEXT_MATCH_ERROR";
+
+    case NPP_FFT_FLAG_ERROR:
+      return "NPP_FFT_FLAG_ERROR";
+
+    case NPP_FFT_ORDER_ERROR:
+      return "NPP_FFT_ORDER_ERROR";
+
+    case NPP_SCALE_RANGE_ERROR:
+      return "NPP_SCALE_RANGE_ERROR";
+
+    case NPP_DATA_TYPE_ERROR:
+      return "NPP_DATA_TYPE_ERROR";
+
+    case NPP_OUT_OFF_RANGE_ERROR:
+      return "NPP_OUT_OFF_RANGE_ERROR";
+
+    case NPP_DIVIDE_BY_ZERO_ERROR:
+      return "NPP_DIVIDE_BY_ZERO_ERROR";
+
+    case NPP_RANGE_ERROR:
+      return "NPP_RANGE_ERROR";
+
+    case NPP_NO_MEMORY_ERROR:
+      return "NPP_NO_MEMORY_ERROR";
+
+    case NPP_ERROR_RESERVED:
+      return "NPP_ERROR_RESERVED";
+
+    case NPP_NO_OPERATION_WARNING:
+      return "NPP_NO_OPERATION_WARNING";
+
+    case NPP_DIVIDE_BY_ZERO_WARNING:
+      return "NPP_DIVIDE_BY_ZERO_WARNING";
+#endif
+
+#if ((NPP_VERSION_MAJOR << 12) + (NPP_VERSION_MINOR << 4)) >= 0x7000
+    /* These are 7.0 or higher */
+    case NPP_OVERFLOW_ERROR:
+      return "NPP_OVERFLOW_ERROR";
+
+    case NPP_CORRUPTED_DATA_ERROR:
+      return "NPP_CORRUPTED_DATA_ERROR";
+#endif
+  }
+
+  return "<unknown>";
+}
+#endif
+
+#ifdef __DRIVER_TYPES_H__
+#ifndef DEVICE_RESET
+#define DEVICE_RESET cudaDeviceReset();
+#endif
+#else
+#ifndef DEVICE_RESET
+#define DEVICE_RESET
+#endif
+#endif
+
+template <typename T>
+void check(T result, char const *const func, const char *const file,
+           int const line) {
+  if (result) {
+    fprintf(stderr, "CUDA error at %s:%d code=%d(%s) \"%s\" \n", file, line,
+            static_cast<unsigned int>(result), _cudaGetErrorEnum(result), func);
+    DEVICE_RESET
+    // Make sure we call CUDA Device Reset before exiting
+    exit(EXIT_FAILURE);
+  }
+}
+
+#ifdef __DRIVER_TYPES_H__
+// This will output the proper CUDA error strings in the event
+// that a CUDA host call returns an error
+#define checkCudaErrors(val) check((val), #val, __FILE__, __LINE__)
+
+// This will output the proper error string when calling cudaGetLastError
+#define getLastCudaError(msg) __getLastCudaError(msg, __FILE__, __LINE__)
+
+inline void __getLastCudaError(const char *errorMessage, const char *file,
+                               const int line) {
+  cudaError_t err = cudaGetLastError();
+
+  if (cudaSuccess != err) {
+    fprintf(stderr,
+            "%s(%i) : getLastCudaError() CUDA error :"
+            " %s : (%d) %s.\n",
+            file, line, errorMessage, static_cast<int>(err),
+            cudaGetErrorString(err));
+    DEVICE_RESET
+    exit(EXIT_FAILURE);
+  }
+}
+
+// This will only print the proper error string when calling cudaGetLastError
+// but not exit program incase error detected.
+#define printLastCudaError(msg) __printLastCudaError(msg, __FILE__, __LINE__)
+
+inline void __printLastCudaError(const char *errorMessage, const char *file,
+                                 const int line) {
+  cudaError_t err = cudaGetLastError();
+
+  if (cudaSuccess != err) {
+    fprintf(stderr,
+            "%s(%i) : getLastCudaError() CUDA error :"
+            " %s : (%d) %s.\n",
+            file, line, errorMessage, static_cast<int>(err),
+            cudaGetErrorString(err));
+  }
+}
+#endif
+
+#ifndef MAX
+#define MAX(a, b) (a > b ? a : b)
+#endif
+
+// Float To Int conversion
+inline int ftoi(float value) {
+  return (value >= 0 ? static_cast<int>(value + 0.5)
+                     : static_cast<int>(value - 0.5));
+}
+
+// Beginning of GPU Architecture definitions
+inline int _ConvertSMVer2Cores(int major, int minor) {
+  // Defines for GPU Architecture types (using the SM version to determine
+  // the # of cores per SM
+  typedef struct {
+    int SM;  // 0xMm (hexidecimal notation), M = SM Major version,
+    // and m = SM minor version
+    int Cores;
+  } sSMtoCores;
+
+  sSMtoCores nGpuArchCoresPerSM[] = {
+      {0x30, 192},
+      {0x32, 192},
+      {0x35, 192},
+      {0x37, 192},
+      {0x50, 128},
+      {0x52, 128},
+      {0x53, 128},
+      {0x60,  64},
+      {0x61, 128},
+      {0x62, 128},
+      {0x70,  64},
+      {0x72,  64},
+      {0x75,  64},
+      {-1, -1}};
+
+  int index = 0;
+
+  while (nGpuArchCoresPerSM[index].SM != -1) {
+    if (nGpuArchCoresPerSM[index].SM == ((major << 4) + minor)) {
+      return nGpuArchCoresPerSM[index].Cores;
+    }
+
+    index++;
+  }
+
+  // If we don't find the values, we default use the previous one
+  // to run properly
+  printf(
+      "MapSMtoCores for SM %d.%d is undefined."
+      "  Default to use %d Cores/SM\n",
+      major, minor, nGpuArchCoresPerSM[index - 1].Cores);
+  return nGpuArchCoresPerSM[index - 1].Cores;
+}
+  // end of GPU Architecture definitions
+
+#ifdef __CUDA_RUNTIME_H__
+// General GPU Device CUDA Initialization
+inline int gpuDeviceInit(int devID) {
+  int device_count;
+  checkCudaErrors(cudaGetDeviceCount(&device_count));
+
+  if (device_count == 0) {
+    fprintf(stderr,
+            "gpuDeviceInit() CUDA error: "
+            "no devices supporting CUDA.\n");
+    exit(EXIT_FAILURE);
+  }
+
+  if (devID < 0) {
+    devID = 0;
+  }
+
+  if (devID > device_count - 1) {
+    fprintf(stderr, "\n");
+    fprintf(stderr, ">> %d CUDA capable GPU device(s) detected. <<\n",
+            device_count);
+    fprintf(stderr,
+            ">> gpuDeviceInit (-device=%d) is not a valid"
+            " GPU device. <<\n",
+            devID);
+    fprintf(stderr, "\n");
+    return -devID;
+  }
+
+  cudaDeviceProp deviceProp;
+  checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devID));
+
+  if (deviceProp.computeMode == cudaComputeModeProhibited) {
+    fprintf(stderr,
+            "Error: device is running in <Compute Mode "
+            "Prohibited>, no threads can use cudaSetDevice().\n");
+    return -1;
+  }
+
+  if (deviceProp.major < 1) {
+    fprintf(stderr, "gpuDeviceInit(): GPU device does not support CUDA.\n");
+    exit(EXIT_FAILURE);
+  }
+
+  checkCudaErrors(cudaSetDevice(devID));
+  printf("gpuDeviceInit() CUDA Device [%d]: \"%s\n", devID, deviceProp.name);
+
+  return devID;
+}
+
+// This function returns the best GPU (with maximum GFLOPS)
+inline int gpuGetMaxGflopsDeviceId() {
+  int current_device = 0, sm_per_multiproc = 0;
+  int max_perf_device = 0;
+  int device_count = 0;
+  int devices_prohibited = 0;
+
+  uint64_t max_compute_perf = 0;
+  cudaDeviceProp deviceProp;
+  checkCudaErrors(cudaGetDeviceCount(&device_count));
+
+  if (device_count == 0) {
+    fprintf(stderr,
+            "gpuGetMaxGflopsDeviceId() CUDA error:"
+            " no devices supporting CUDA.\n");
+    exit(EXIT_FAILURE);
+  }
+
+  // Find the best CUDA capable GPU device
+  current_device = 0;
+
+  while (current_device < device_count) {
+    cudaGetDeviceProperties(&deviceProp, current_device);
+
+    // If this GPU is not running on Compute Mode prohibited,
+    // then we can add it to the list
+    if (deviceProp.computeMode != cudaComputeModeProhibited) {
+      if (deviceProp.major == 9999 && deviceProp.minor == 9999) {
+        sm_per_multiproc = 1;
+      } else {
+        sm_per_multiproc =
+            _ConvertSMVer2Cores(deviceProp.major, deviceProp.minor);
+      }
+
+      uint64_t compute_perf = (uint64_t)deviceProp.multiProcessorCount *
+                              sm_per_multiproc * deviceProp.clockRate;
+
+      if (compute_perf > max_compute_perf) {
+        max_compute_perf = compute_perf;
+        max_perf_device = current_device;
+      }
+    } else {
+      devices_prohibited++;
+    }
+
+    ++current_device;
+  }
+
+  if (devices_prohibited == device_count) {
+    fprintf(stderr,
+            "gpuGetMaxGflopsDeviceId() CUDA error:"
+            " all devices have compute mode prohibited.\n");
+    exit(EXIT_FAILURE);
+  }
+
+  return max_perf_device;
+}
+
+// Initialization code to find the best CUDA Device
+inline int findCudaDevice(int argc, const char **argv) {
+  cudaDeviceProp deviceProp;
+  int devID = 0;
+
+  // If the command-line has a device number specified, use it
+  if (checkCmdLineFlag(argc, argv, "device")) {
+    devID = getCmdLineArgumentInt(argc, argv, "device=");
+
+    if (devID < 0) {
+      printf("Invalid command line parameter\n ");
+      exit(EXIT_FAILURE);
+    } else {
+      devID = gpuDeviceInit(devID);
+
+      if (devID < 0) {
+        printf("exiting...\n");
+        exit(EXIT_FAILURE);
+      }
+    }
+  } else {
+    // Otherwise pick the device with highest Gflops/s
+    devID = gpuGetMaxGflopsDeviceId();
+    checkCudaErrors(cudaSetDevice(devID));
+    checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devID));
+    printf("GPU Device %d: \"%s\" with compute capability %d.%d\n\n", devID,
+           deviceProp.name, deviceProp.major, deviceProp.minor);
+  }
+
+  return devID;
+}
+
+inline int findIntegratedGPU() {
+  int current_device = 0;
+  int device_count = 0;
+  int devices_prohibited = 0;
+
+  cudaDeviceProp deviceProp;
+  checkCudaErrors(cudaGetDeviceCount(&device_count));
+
+  if (device_count == 0) {
+    fprintf(stderr, "CUDA error: no devices supporting CUDA.\n");
+    exit(EXIT_FAILURE);
+  }
+
+  // Find the integrated GPU which is compute capable
+  while (current_device < device_count) {
+    cudaGetDeviceProperties(&deviceProp, current_device);
+
+    // If GPU is integrated and is not running on Compute Mode prohibited,
+    // then cuda can map to GLES resource
+    if (deviceProp.integrated &&
+        (deviceProp.computeMode != cudaComputeModeProhibited)) {
+      checkCudaErrors(cudaSetDevice(current_device));
+      checkCudaErrors(cudaGetDeviceProperties(&deviceProp, current_device));
+      printf("GPU Device %d: \"%s\" with compute capability %d.%d\n\n",
+             current_device, deviceProp.name, deviceProp.major,
+             deviceProp.minor);
+
+      return current_device;
+    } else {
+      devices_prohibited++;
+    }
+
+    current_device++;
+  }
+
+  if (devices_prohibited == device_count) {
+    fprintf(stderr,
+            "CUDA error:"
+            " No GLES-CUDA Interop capable GPU found.\n");
+    exit(EXIT_FAILURE);
+  }
+
+  return -1;
+}
+
+// General check for CUDA GPU SM Capabilities
+inline bool checkCudaCapabilities(int major_version, int minor_version) {
+  cudaDeviceProp deviceProp;
+  deviceProp.major = 0;
+  deviceProp.minor = 0;
+  int dev;
+
+  checkCudaErrors(cudaGetDevice(&dev));
+  checkCudaErrors(cudaGetDeviceProperties(&deviceProp, dev));
+
+  if ((deviceProp.major > major_version) ||
+      (deviceProp.major == major_version &&
+       deviceProp.minor >= minor_version)) {
+    printf("  Device %d: <%16s >, Compute SM %d.%d detected\n", dev,
+           deviceProp.name, deviceProp.major, deviceProp.minor);
+    return true;
+  } else {
+    printf(
+        "  No GPU device was found that can support "
+        "CUDA compute capability %d.%d.\n",
+        major_version, minor_version);
+    return false;
+  }
+}
+#endif
+
+  // end of CUDA Helper Functions
+
+#endif  // COMMON_HELPER_CUDA_H_
diff --git a/notebooks/asian_barrier_option/helper_string.h b/notebooks/asian_barrier_option/helper_string.h
new file mode 100644
index 00000000..77864b8f
--- /dev/null
+++ b/notebooks/asian_barrier_option/helper_string.h
@@ -0,0 +1,683 @@
+/**
+ * Copyright 1993-2013 NVIDIA Corporation.  All rights reserved.
+ *
+ * Please refer to the NVIDIA end user license agreement (EULA) associated
+ * with this source code for terms and conditions that govern your use of
+ * this software. Any use, reproduction, disclosure, or distribution of
+ * this software and related documentation outside the terms of the EULA
+ * is strictly prohibited.
+ *
+ */
+
+// These are helper functions for the SDK samples (string parsing, timers, etc)
+#ifndef COMMON_HELPER_STRING_H_
+#define COMMON_HELPER_STRING_H_
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <fstream>
+#include <string>
+
+#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
+#ifndef _CRT_SECURE_NO_DEPRECATE
+#define _CRT_SECURE_NO_DEPRECATE
+#endif
+#ifndef STRCASECMP
+#define STRCASECMP _stricmp
+#endif
+#ifndef STRNCASECMP
+#define STRNCASECMP _strnicmp
+#endif
+#ifndef STRCPY
+#define STRCPY(sFilePath, nLength, sPath) strcpy_s(sFilePath, nLength, sPath)
+#endif
+
+#ifndef FOPEN
+#define FOPEN(fHandle, filename, mode) fopen_s(&fHandle, filename, mode)
+#endif
+#ifndef FOPEN_FAIL
+#define FOPEN_FAIL(result) (result != 0)
+#endif
+#ifndef SSCANF
+#define SSCANF sscanf_s
+#endif
+#ifndef SPRINTF
+#define SPRINTF sprintf_s
+#endif
+#else  // Linux Includes
+#include <string.h>
+#include <strings.h>
+
+#ifndef STRCASECMP
+#define STRCASECMP strcasecmp
+#endif
+#ifndef STRNCASECMP
+#define STRNCASECMP strncasecmp
+#endif
+#ifndef STRCPY
+#define STRCPY(sFilePath, nLength, sPath) strcpy(sFilePath, sPath)
+#endif
+
+#ifndef FOPEN
+#define FOPEN(fHandle, filename, mode) (fHandle = fopen(filename, mode))
+#endif
+#ifndef FOPEN_FAIL
+#define FOPEN_FAIL(result) (result == NULL)
+#endif
+#ifndef SSCANF
+#define SSCANF sscanf
+#endif
+#ifndef SPRINTF
+#define SPRINTF sprintf
+#endif
+#endif
+
+#ifndef EXIT_WAIVED
+#define EXIT_WAIVED 2
+#endif
+
+// CUDA Utility Helper Functions
+inline int stringRemoveDelimiter(char delimiter, const char *string) {
+  int string_start = 0;
+
+  while (string[string_start] == delimiter) {
+    string_start++;
+  }
+
+  if (string_start >= static_cast<int>(strlen(string) - 1)) {
+    return 0;
+  }
+
+  return string_start;
+}
+
+inline int getFileExtension(char *filename, char **extension) {
+  int string_length = static_cast<int>(strlen(filename));
+
+  while (filename[string_length--] != '.') {
+    if (string_length == 0) break;
+  }
+
+  if (string_length > 0) string_length += 2;
+
+  if (string_length == 0)
+    *extension = NULL;
+  else
+    *extension = &filename[string_length];
+
+  return string_length;
+}
+
+inline bool checkCmdLineFlag(const int argc, const char **argv,
+                             const char *string_ref) {
+  bool bFound = false;
+
+  if (argc >= 1) {
+    for (int i = 1; i < argc; i++) {
+      int string_start = stringRemoveDelimiter('-', argv[i]);
+      const char *string_argv = &argv[i][string_start];
+
+      const char *equal_pos = strchr(string_argv, '=');
+      int argv_length = static_cast<int>(
+          equal_pos == 0 ? strlen(string_argv) : equal_pos - string_argv);
+
+      int length = static_cast<int>(strlen(string_ref));
+
+      if (length == argv_length &&
+          !STRNCASECMP(string_argv, string_ref, length)) {
+        bFound = true;
+        continue;
+      }
+    }
+  }
+
+  return bFound;
+}
+
+// This function wraps the CUDA Driver API into a template function
+template <class T>
+inline bool getCmdLineArgumentValue(const int argc, const char **argv,
+                                    const char *string_ref, T *value) {
+  bool bFound = false;
+
+  if (argc >= 1) {
+    for (int i = 1; i < argc; i++) {
+      int string_start = stringRemoveDelimiter('-', argv[i]);
+      const char *string_argv = &argv[i][string_start];
+      int length = static_cast<int>(strlen(string_ref));
+
+      if (!STRNCASECMP(string_argv, string_ref, length)) {
+        if (length + 1 <= static_cast<int>(strlen(string_argv))) {
+          int auto_inc = (string_argv[length] == '=') ? 1 : 0;
+          *value = (T)atoi(&string_argv[length + auto_inc]);
+        }
+
+        bFound = true;
+        i = argc;
+      }
+    }
+  }
+
+  return bFound;
+}
+
+inline int getCmdLineArgumentInt(const int argc, const char **argv,
+                                 const char *string_ref) {
+  bool bFound = false;
+  int value = -1;
+
+  if (argc >= 1) {
+    for (int i = 1; i < argc; i++) {
+      int string_start = stringRemoveDelimiter('-', argv[i]);
+      const char *string_argv = &argv[i][string_start];
+      int length = static_cast<int>(strlen(string_ref));
+
+      if (!STRNCASECMP(string_argv, string_ref, length)) {
+        if (length + 1 <= static_cast<int>(strlen(string_argv))) {
+          int auto_inc = (string_argv[length] == '=') ? 1 : 0;
+          value = atoi(&string_argv[length + auto_inc]);
+        } else {
+          value = 0;
+        }
+
+        bFound = true;
+        continue;
+      }
+    }
+  }
+
+  if (bFound) {
+    return value;
+  } else {
+    return 0;
+  }
+}
+
+inline float getCmdLineArgumentFloat(const int argc, const char **argv,
+                                     const char *string_ref) {
+  bool bFound = false;
+  float value = -1;
+
+  if (argc >= 1) {
+    for (int i = 1; i < argc; i++) {
+      int string_start = stringRemoveDelimiter('-', argv[i]);
+      const char *string_argv = &argv[i][string_start];
+      int length = static_cast<int>(strlen(string_ref));
+
+      if (!STRNCASECMP(string_argv, string_ref, length)) {
+        if (length + 1 <= static_cast<int>(strlen(string_argv))) {
+          int auto_inc = (string_argv[length] == '=') ? 1 : 0;
+          value = static_cast<float>(atof(&string_argv[length + auto_inc]));
+        } else {
+          value = 0.f;
+        }
+
+        bFound = true;
+        continue;
+      }
+    }
+  }
+
+  if (bFound) {
+    return value;
+  } else {
+    return 0;
+  }
+}
+
+inline bool getCmdLineArgumentString(const int argc, const char **argv,
+                                     const char *string_ref,
+                                     char **string_retval) {
+  bool bFound = false;
+
+  if (argc >= 1) {
+    for (int i = 1; i < argc; i++) {
+      int string_start = stringRemoveDelimiter('-', argv[i]);
+      char *string_argv = const_cast<char*>(&argv[i][string_start]);
+      int length = static_cast<int>(strlen(string_ref));
+
+      if (!STRNCASECMP(string_argv, string_ref, length)) {
+        *string_retval = &string_argv[length + 1];
+        bFound = true;
+        continue;
+      }
+    }
+  }
+
+  if (!bFound) {
+    *string_retval = NULL;
+  }
+
+  return bFound;
+}
+
+//////////////////////////////////////////////////////////////////////////////
+//! Find the path for a file assuming that
+//! files are found in the searchPath.
+//!
+//! @return the path if succeeded, otherwise 0
+//! @param filename         name of the file
+//! @param executable_path  optional absolute path of the executable
+//////////////////////////////////////////////////////////////////////////////
+inline char *sdkFindFilePath(const char *filename,
+                             const char *executable_path) {
+  // <executable_name> defines a variable that is replaced with the name of the
+  // executable
+
+  // Typical relative search paths to locate needed companion files (e.g. sample
+  // input data, or JIT source files) The origin for the relative search may be
+  // the .exe file, a .bat file launching an .exe, a browser .exe launching the
+  // .exe or .bat, etc
+  const char *searchPath[] = {
+      "./",  // same dir
+      "./<executable_name>_data_files/",
+      "./common/",                      // "/common/" subdir
+      "./common/data/",                 // "/common/data/" subdir
+      "./data/",                        // "/data/" subdir
+      "./src/",                         // "/src/" subdir
+      "./src/<executable_name>/data/",  // "/src/<executable_name>/data/" subdir
+      "./inc/",                         // "/inc/" subdir
+      "./0_Simple/",                    // "/0_Simple/" subdir
+      "./1_Utilities/",                 // "/1_Utilities/" subdir
+      "./2_Graphics/",                  // "/2_Graphics/" subdir
+      "./3_Imaging/",                   // "/3_Imaging/" subdir
+      "./4_Finance/",                   // "/4_Finance/" subdir
+      "./5_Simulations/",               // "/5_Simulations/" subdir
+      "./6_Advanced/",                  // "/6_Advanced/" subdir
+      "./7_CUDALibraries/",             // "/7_CUDALibraries/" subdir
+      "./8_Android/",                   // "/8_Android/" subdir
+      "./samples/",                     // "/samples/" subdir
+
+      "./0_Simple/<executable_name>/data/",  // "/0_Simple/<executable_name>/data/"
+                                             // subdir
+      "./1_Utilities/<executable_name>/data/",  // "/1_Utilities/<executable_name>/data/"
+                                                // subdir
+      "./2_Graphics/<executable_name>/data/",  // "/2_Graphics/<executable_name>/data/"
+                                               // subdir
+      "./3_Imaging/<executable_name>/data/",  // "/3_Imaging/<executable_name>/data/"
+                                              // subdir
+      "./4_Finance/<executable_name>/data/",  // "/4_Finance/<executable_name>/data/"
+                                              // subdir
+      "./5_Simulations/<executable_name>/data/",  // "/5_Simulations/<executable_name>/data/"
+                                                  // subdir
+      "./6_Advanced/<executable_name>/data/",  // "/6_Advanced/<executable_name>/data/"
+                                               // subdir
+      "./7_CUDALibraries/<executable_name>/",  // "/7_CUDALibraries/<executable_name>/"
+                                               // subdir
+      "./7_CUDALibraries/<executable_name>/data/",  // "/7_CUDALibraries/<executable_name>/data/"
+                                                    // subdir
+
+      "../",              // up 1 in tree
+      "../common/",       // up 1 in tree, "/common/" subdir
+      "../common/data/",  // up 1 in tree, "/common/data/" subdir
+      "../data/",         // up 1 in tree, "/data/" subdir
+      "../src/",          // up 1 in tree, "/src/" subdir
+      "../inc/",          // up 1 in tree, "/inc/" subdir
+
+      "../0_Simple/<executable_name>/data/",  // up 1 in tree,
+                                              // "/0_Simple/<executable_name>/"
+                                              // subdir
+      "../1_Utilities/<executable_name>/data/",  // up 1 in tree,
+                                                 // "/1_Utilities/<executable_name>/"
+                                                 // subdir
+      "../2_Graphics/<executable_name>/data/",  // up 1 in tree,
+                                                // "/2_Graphics/<executable_name>/"
+                                                // subdir
+      "../3_Imaging/<executable_name>/data/",  // up 1 in tree,
+                                               // "/3_Imaging/<executable_name>/"
+                                               // subdir
+      "../4_Finance/<executable_name>/data/",  // up 1 in tree,
+                                               // "/4_Finance/<executable_name>/"
+                                               // subdir
+      "../5_Simulations/<executable_name>/data/",  // up 1 in tree,
+                                                   // "/5_Simulations/<executable_name>/"
+                                                   // subdir
+      "../6_Advanced/<executable_name>/data/",  // up 1 in tree,
+                                                // "/6_Advanced/<executable_name>/"
+                                                // subdir
+      "../7_CUDALibraries/<executable_name>/data/",  // up 1 in tree,
+                                                     // "/7_CUDALibraries/<executable_name>/"
+                                                     // subdir
+      "../8_Android/<executable_name>/data/",  // up 1 in tree,
+                                               // "/8_Android/<executable_name>/"
+                                               // subdir
+      "../samples/<executable_name>/data/",  // up 1 in tree,
+                                             // "/samples/<executable_name>/"
+                                             // subdir
+      "../../",                              // up 2 in tree
+      "../../common/",                       // up 2 in tree, "/common/" subdir
+      "../../common/data/",  // up 2 in tree, "/common/data/" subdir
+      "../../data/",         // up 2 in tree, "/data/" subdir
+      "../../src/",          // up 2 in tree, "/src/" subdir
+      "../../inc/",          // up 2 in tree, "/inc/" subdir
+      "../../sandbox/<executable_name>/data/",  // up 2 in tree,
+                                                // "/sandbox/<executable_name>/"
+                                                // subdir
+      "../../0_Simple/<executable_name>/data/",  // up 2 in tree,
+                                                 // "/0_Simple/<executable_name>/"
+                                                 // subdir
+      "../../1_Utilities/<executable_name>/data/",  // up 2 in tree,
+                                                    // "/1_Utilities/<executable_name>/"
+                                                    // subdir
+      "../../2_Graphics/<executable_name>/data/",  // up 2 in tree,
+                                                   // "/2_Graphics/<executable_name>/"
+                                                   // subdir
+      "../../3_Imaging/<executable_name>/data/",  // up 2 in tree,
+                                                  // "/3_Imaging/<executable_name>/"
+                                                  // subdir
+      "../../4_Finance/<executable_name>/data/",  // up 2 in tree,
+                                                  // "/4_Finance/<executable_name>/"
+                                                  // subdir
+      "../../5_Simulations/<executable_name>/data/",  // up 2 in tree,
+                                                      // "/5_Simulations/<executable_name>/"
+                                                      // subdir
+      "../../6_Advanced/<executable_name>/data/",  // up 2 in tree,
+                                                   // "/6_Advanced/<executable_name>/"
+                                                   // subdir
+      "../../7_CUDALibraries/<executable_name>/data/",  // up 2 in tree,
+                                                        // "/7_CUDALibraries/<executable_name>/"
+                                                        // subdir
+      "../../8_Android/<executable_name>/data/",  // up 2 in tree,
+                                                  // "/8_Android/<executable_name>/"
+                                                  // subdir
+      "../../samples/<executable_name>/data/",  // up 2 in tree,
+                                                // "/samples/<executable_name>/"
+                                                // subdir
+      "../../../",                              // up 3 in tree
+      "../../../src/<executable_name>/",        // up 3 in tree,
+                                          // "/src/<executable_name>/" subdir
+      "../../../src/<executable_name>/data/",  // up 3 in tree,
+                                               // "/src/<executable_name>/data/"
+                                               // subdir
+      "../../../src/<executable_name>/src/",   // up 3 in tree,
+                                               // "/src/<executable_name>/src/"
+                                               // subdir
+      "../../../src/<executable_name>/inc/",   // up 3 in tree,
+                                               // "/src/<executable_name>/inc/"
+                                               // subdir
+      "../../../sandbox/<executable_name>/",   // up 3 in tree,
+                                               // "/sandbox/<executable_name>/"
+                                               // subdir
+      "../../../sandbox/<executable_name>/data/",  // up 3 in tree,
+                                                   // "/sandbox/<executable_name>/data/"
+                                                   // subdir
+      "../../../sandbox/<executable_name>/src/",  // up 3 in tree,
+                                                  // "/sandbox/<executable_name>/src/"
+                                                  // subdir
+      "../../../sandbox/<executable_name>/inc/",  // up 3 in tree,
+                                                  // "/sandbox/<executable_name>/inc/"
+                                                  // subdir
+      "../../../0_Simple/<executable_name>/data/",  // up 3 in tree,
+                                                    // "/0_Simple/<executable_name>/"
+                                                    // subdir
+      "../../../1_Utilities/<executable_name>/data/",  // up 3 in tree,
+                                                       // "/1_Utilities/<executable_name>/"
+                                                       // subdir
+      "../../../2_Graphics/<executable_name>/data/",  // up 3 in tree,
+                                                      // "/2_Graphics/<executable_name>/"
+                                                      // subdir
+      "../../../3_Imaging/<executable_name>/data/",  // up 3 in tree,
+                                                     // "/3_Imaging/<executable_name>/"
+                                                     // subdir
+      "../../../4_Finance/<executable_name>/data/",  // up 3 in tree,
+                                                     // "/4_Finance/<executable_name>/"
+                                                     // subdir
+      "../../../5_Simulations/<executable_name>/data/",  // up 3 in tree,
+                                                         // "/5_Simulations/<executable_name>/"
+                                                         // subdir
+      "../../../6_Advanced/<executable_name>/data/",  // up 3 in tree,
+                                                      // "/6_Advanced/<executable_name>/"
+                                                      // subdir
+      "../../../7_CUDALibraries/<executable_name>/data/",  // up 3 in tree,
+                                                           // "/7_CUDALibraries/<executable_name>/"
+                                                           // subdir
+      "../../../8_Android/<executable_name>/data/",  // up 3 in tree,
+                                                     // "/8_Android/<executable_name>/"
+                                                     // subdir
+      "../../../0_Simple/<executable_name>/",  // up 3 in tree,
+                                               // "/0_Simple/<executable_name>/"
+                                               // subdir
+      "../../../1_Utilities/<executable_name>/",  // up 3 in tree,
+                                                  // "/1_Utilities/<executable_name>/"
+                                                  // subdir
+      "../../../2_Graphics/<executable_name>/",  // up 3 in tree,
+                                                 // "/2_Graphics/<executable_name>/"
+                                                 // subdir
+      "../../../3_Imaging/<executable_name>/",  // up 3 in tree,
+                                                // "/3_Imaging/<executable_name>/"
+                                                // subdir
+      "../../../4_Finance/<executable_name>/",  // up 3 in tree,
+                                                // "/4_Finance/<executable_name>/"
+                                                // subdir
+      "../../../5_Simulations/<executable_name>/",  // up 3 in tree,
+                                                    // "/5_Simulations/<executable_name>/"
+                                                    // subdir
+      "../../../6_Advanced/<executable_name>/",  // up 3 in tree,
+                                                 // "/6_Advanced/<executable_name>/"
+                                                 // subdir
+      "../../../7_CUDALibraries/<executable_name>/",  // up 3 in tree,
+                                                      // "/7_CUDALibraries/<executable_name>/"
+                                                      // subdir
+      "../../../8_Android/<executable_name>/",  // up 3 in tree,
+                                                // "/8_Android/<executable_name>/"
+                                                // subdir
+      "../../../samples/<executable_name>/data/",  // up 3 in tree,
+                                                   // "/samples/<executable_name>/"
+                                                   // subdir
+      "../../../common/",       // up 3 in tree, "../../../common/" subdir
+      "../../../common/data/",  // up 3 in tree, "../../../common/data/" subdir
+      "../../../data/",         // up 3 in tree, "../../../data/" subdir
+      "../../../../",           // up 4 in tree
+      "../../../../src/<executable_name>/",  // up 4 in tree,
+                                             // "/src/<executable_name>/" subdir
+      "../../../../src/<executable_name>/data/",  // up 4 in tree,
+                                                  // "/src/<executable_name>/data/"
+                                                  // subdir
+      "../../../../src/<executable_name>/src/",  // up 4 in tree,
+                                                 // "/src/<executable_name>/src/"
+                                                 // subdir
+      "../../../../src/<executable_name>/inc/",  // up 4 in tree,
+                                                 // "/src/<executable_name>/inc/"
+                                                 // subdir
+      "../../../../sandbox/<executable_name>/",  // up 4 in tree,
+                                                 // "/sandbox/<executable_name>/"
+                                                 // subdir
+      "../../../../sandbox/<executable_name>/data/",  // up 4 in tree,
+                                                      // "/sandbox/<executable_name>/data/"
+                                                      // subdir
+      "../../../../sandbox/<executable_name>/src/",  // up 4 in tree,
+                                                     // "/sandbox/<executable_name>/src/"
+                                                     // subdir
+      "../../../../sandbox/<executable_name>/inc/",  // up 4 in tree,
+                                                     // "/sandbox/<executable_name>/inc/"
+                                                     // subdir
+      "../../../../0_Simple/<executable_name>/data/",  // up 4 in tree,
+                                                       // "/0_Simple/<executable_name>/"
+                                                       // subdir
+      "../../../../1_Utilities/<executable_name>/data/",  // up 4 in tree,
+                                                          // "/1_Utilities/<executable_name>/"
+                                                          // subdir
+      "../../../../2_Graphics/<executable_name>/data/",  // up 4 in tree,
+                                                         // "/2_Graphics/<executable_name>/"
+                                                         // subdir
+      "../../../../3_Imaging/<executable_name>/data/",  // up 4 in tree,
+                                                        // "/3_Imaging/<executable_name>/"
+                                                        // subdir
+      "../../../../4_Finance/<executable_name>/data/",  // up 4 in tree,
+                                                        // "/4_Finance/<executable_name>/"
+                                                        // subdir
+      "../../../../5_Simulations/<executable_name>/data/",  // up 4 in tree,
+                                                            // "/5_Simulations/<executable_name>/"
+                                                            // subdir
+      "../../../../6_Advanced/<executable_name>/data/",  // up 4 in tree,
+                                                         // "/6_Advanced/<executable_name>/"
+                                                         // subdir
+      "../../../../7_CUDALibraries/<executable_name>/data/",  // up 4 in tree,
+                                                              // "/7_CUDALibraries/<executable_name>/"
+                                                              // subdir
+      "../../../../8_Android/<executable_name>/data/",  // up 4 in tree,
+                                                        // "/8_Android/<executable_name>/"
+                                                        // subdir
+      "../../../../0_Simple/<executable_name>/",  // up 4 in tree,
+                                                  // "/0_Simple/<executable_name>/"
+                                                  // subdir
+      "../../../../1_Utilities/<executable_name>/",  // up 4 in tree,
+                                                     // "/1_Utilities/<executable_name>/"
+                                                     // subdir
+      "../../../../2_Graphics/<executable_name>/",  // up 4 in tree,
+                                                    // "/2_Graphics/<executable_name>/"
+                                                    // subdir
+      "../../../../3_Imaging/<executable_name>/",  // up 4 in tree,
+                                                   // "/3_Imaging/<executable_name>/"
+                                                   // subdir
+      "../../../../4_Finance/<executable_name>/",  // up 4 in tree,
+                                                   // "/4_Finance/<executable_name>/"
+                                                   // subdir
+      "../../../../5_Simulations/<executable_name>/",  // up 4 in tree,
+                                                       // "/5_Simulations/<executable_name>/"
+                                                       // subdir
+      "../../../../6_Advanced/<executable_name>/",  // up 4 in tree,
+                                                    // "/6_Advanced/<executable_name>/"
+                                                    // subdir
+      "../../../../7_CUDALibraries/<executable_name>/",  // up 4 in tree,
+                                                         // "/7_CUDALibraries/<executable_name>/"
+                                                         // subdir
+      "../../../../8_Android/<executable_name>/",  // up 4 in tree,
+                                                   // "/8_Android/<executable_name>/"
+                                                   // subdir
+      "../../../../samples/<executable_name>/data/",  // up 4 in tree,
+                                                      // "/samples/<executable_name>/"
+                                                      // subdir
+      "../../../../common/",       // up 4 in tree, "../../../common/" subdir
+      "../../../../common/data/",  // up 4 in tree, "../../../common/data/"
+                                   // subdir
+      "../../../../data/",         // up 4 in tree, "../../../data/" subdir
+      "../../../../../",           // up 5 in tree
+      "../../../../../src/<executable_name>/",  // up 5 in tree,
+                                                // "/src/<executable_name>/"
+                                                // subdir
+      "../../../../../src/<executable_name>/data/",  // up 5 in tree,
+                                                     // "/src/<executable_name>/data/"
+                                                     // subdir
+      "../../../../../src/<executable_name>/src/",  // up 5 in tree,
+                                                    // "/src/<executable_name>/src/"
+                                                    // subdir
+      "../../../../../src/<executable_name>/inc/",  // up 5 in tree,
+                                                    // "/src/<executable_name>/inc/"
+                                                    // subdir
+      "../../../../../sandbox/<executable_name>/",  // up 5 in tree,
+                                                    // "/sandbox/<executable_name>/"
+                                                    // subdir
+      "../../../../../sandbox/<executable_name>/data/",  // up 5 in tree,
+                                                         // "/sandbox/<executable_name>/data/"
+                                                         // subdir
+      "../../../../../sandbox/<executable_name>/src/",  // up 5 in tree,
+                                                        // "/sandbox/<executable_name>/src/"
+                                                        // subdir
+      "../../../../../sandbox/<executable_name>/inc/",  // up 5 in tree,
+                                                        // "/sandbox/<executable_name>/inc/"
+                                                        // subdir
+      "../../../../../0_Simple/<executable_name>/data/",  // up 5 in tree,
+                                                          // "/0_Simple/<executable_name>/"
+                                                          // subdir
+      "../../../../../1_Utilities/<executable_name>/data/",  // up 5 in tree,
+                                                             // "/1_Utilities/<executable_name>/"
+                                                             // subdir
+      "../../../../../2_Graphics/<executable_name>/data/",  // up 5 in tree,
+                                                            // "/2_Graphics/<executable_name>/"
+                                                            // subdir
+      "../../../../../3_Imaging/<executable_name>/data/",  // up 5 in tree,
+                                                           // "/3_Imaging/<executable_name>/"
+                                                           // subdir
+      "../../../../../4_Finance/<executable_name>/data/",  // up 5 in tree,
+                                                           // "/4_Finance/<executable_name>/"
+                                                           // subdir
+      "../../../../../5_Simulations/<executable_name>/data/",  // up 5 in tree,
+                                                               // "/5_Simulations/<executable_name>/"
+                                                               // subdir
+      "../../../../../6_Advanced/<executable_name>/data/",  // up 5 in tree,
+                                                            // "/6_Advanced/<executable_name>/"
+                                                            // subdir
+      "../../../../../7_CUDALibraries/<executable_name>/data/",  // up 5 in
+                                                                 // tree,
+                                                                 // "/7_CUDALibraries/<executable_name>/"
+                                                                 // subdir
+      "../../../../../8_Android/<executable_name>/data/",  // up 5 in tree,
+                                                           // "/8_Android/<executable_name>/"
+                                                           // subdir
+      "../../../../../samples/<executable_name>/data/",  // up 5 in tree,
+                                                         // "/samples/<executable_name>/"
+                                                         // subdir
+      "../../../../../common/",       // up 5 in tree, "../../../common/" subdir
+      "../../../../../common/data/",  // up 5 in tree, "../../../common/data/"
+                                      // subdir
+  };
+
+  // Extract the executable name
+  std::string executable_name;
+
+  if (executable_path != 0) {
+    executable_name = std::string(executable_path);
+
+#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
+    // Windows path delimiter
+    size_t delimiter_pos = executable_name.find_last_of('\\');
+    executable_name.erase(0, delimiter_pos + 1);
+
+    if (executable_name.rfind(".exe") != std::string::npos) {
+      // we strip .exe, only if the .exe is found
+      executable_name.resize(executable_name.size() - 4);
+    }
+
+#else
+    // Linux & OSX path delimiter
+    size_t delimiter_pos = executable_name.find_last_of('/');
+    executable_name.erase(0, delimiter_pos + 1);
+#endif
+  }
+
+  // Loop over all search paths and return the first hit
+  for (unsigned int i = 0; i < sizeof(searchPath) / sizeof(char *); ++i) {
+    std::string path(searchPath[i]);
+    size_t executable_name_pos = path.find("<executable_name>");
+
+    // If there is executable_name variable in the searchPath
+    // replace it with the value
+    if (executable_name_pos != std::string::npos) {
+      if (executable_path != 0) {
+        path.replace(executable_name_pos, strlen("<executable_name>"),
+                     executable_name);
+      } else {
+        // Skip this path entry if no executable argument is given
+        continue;
+      }
+    }
+
+#ifdef _DEBUG
+    printf("sdkFindFilePath <%s> in %s\n", filename, path.c_str());
+#endif
+
+    // Test if the file exists
+    path.append(filename);
+    FILE *fp;
+    FOPEN(fp, path.c_str(), "rb");
+
+    if (fp != NULL) {
+      fclose(fp);
+      // File found
+      // returning an allocated array here for backwards compatibility reasons
+      char *file_path = reinterpret_cast<char *>(malloc(path.length() + 1));
+      STRCPY(file_path, path.length() + 1, path.c_str());
+      return file_path;
+    }
+
+    if (fp) {
+      fclose(fp);
+    }
+  }
+
+  // File not found
+  return 0;
+}
+
+#endif  // COMMON_HELPER_STRING_H_
diff --git a/notebooks/asian_barrier_option/index.ipynb b/notebooks/asian_barrier_option/index.ipynb
new file mode 100644
index 00000000..2a5c8038
--- /dev/null
+++ b/notebooks/asian_barrier_option/index.ipynb
@@ -0,0 +1,79 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Asian Barrier Options Pricing using GPU Acceleration\n",
+    "\n",
+    "\n",
+    "### Introduction\n",
+    "\n",
+    "The European and American Options price can be estimated accurately by the efficient [Black–Scholes model](https://en.wikipedia.org/wiki/Black%E2%80%93Scholes_model). Options like [Barrier Option](https://en.wikipedia.org/wiki/Barrier_option) and [Basket Option](https://en.wikipedia.org/wiki/Basket_option) have a complicated structure with no simple analytical solution. The Monte Carlo simulation is an effective way to price them. To get an accurate price with a small variance, a large number of simulation paths are needed which is computationally intensive. Luckily, each of the simulation paths are independent and we can take advantage of the multiple core GPU to accelerate the computation. Using GPU can speedup the computation by orders of magnitude due to the parallelization of the independent paths. But even that is still not fast enough. Recently, [Deep learning derivatives method](https://arxiv.org/pdf/1809.02233.pdf) was introduced to value derivatives and achieves speedup even higher than the former.  \n",
+    "\n",
+    "In this tutorial, we are going to price the [Down-and-Out](https://www.investopedia.com/terms/d/daoo.asp) [Asian](https://www.investopedia.com/terms/a/asianoption.asp) [Barrier](https://www.investopedia.com/terms/b/barrieroption.asp) [Call Option](https://www.investopedia.com/terms/c/calloption.asp) :\n",
+    "\n",
+    "    \n",
+    "### Barrier Option pricing\n",
+    "\n",
+    "Asian Barrier Option is a mixture of [Asian Option](https://en.wikipedia.org/wiki/Asian_option) and [Barrier Option](https://en.wikipedia.org/wiki/Barrier_option). The price depends on the average underlying Asset Price `S`, the Strick Price `K` and the Barrier Price `B`. There are 4 types of Barrier Options:-\n",
+    "   * [Up-and-out](https://www.investopedia.com/terms/u/up-and-outoption.asp): spot price starts below the barrier level and has to move up for the option to be knocked out.\n",
+    "   * [Down-and-out](https://www.investopedia.com/terms/d/daoo.asp): spot price starts above the barrier level and has to move down for the option to be knocked out.\n",
+    "   * [Up-and-in](https://www.investopedia.com/terms/u/up-and-inoption.asp): spot price starts below the barrier level and has to move up for the option to become activated.\n",
+    "   * [Down-and-in](https://www.investopedia.com/terms/d/daio.asp): spot price starts above the barrier level and has to move down for the option to become activated.\n",
+    "\n",
+    "Without loss of generality, in this tutorial we will use the [Down-and-Out Call Discretized Asian Barrier Option](https://ieeexplore.ieee.org/document/6327776/metrics#metrics) as an example. The option will be void if the average price of the underlying asset goes below the barrier. The asset Spot Price `S` is usually modeled as [Geometric Brownian motion](https://en.wikipedia.org/wiki/Geometric_Brownian_motion), which has 3 free parameters:- [Spot Price](https://www.investopedia.com/terms/s/spotprice.asp), [Percent Volatility](https://www.investopedia.com/terms/v/volatility.asp) and the [Percent Drift](https://en.wikipedia.org/wiki/Stochastic_drift). The price of the option will be the expected profit at the maturity discount to the current value.\n",
+    "\n",
+    "### Preliminary \n",
+    "\n",
+    "You need to build a docker image to run the examples. \n",
+    "\n",
+    "```bash\n",
+    "cd docker\n",
+    "build -f Dockerfile -t option .\n",
+    "# launch your nvidia docker container and expose the port for Jupyterlab\n",
+    "```\n",
+    "\n",
+    "### Outline \n",
+    "\n",
+    "This tutorial is organized as following notebooks\n",
+    "\n",
+    "1. [Use Python GPU libraries to accelerate the Monte Carlo pricing on the GPU](./mc_pricing.ipynb)\n",
+    "2. [Use the Monte Carlo pricing dynamic dataset to train an Option Pricing Neural Network Model](./deep_learning_option_1.ipynb)\n",
+    "3. [Use the Monte Carlo pricing staic dataset to train an Option Pricing Neural Network Model and do inference](./deep_learning_option_2.ipynb)\n",
+    "4. [Train an Asian Barrier Option Pricing Neural Network Model with NeMo](./deep_learning_nemo.ipynb)\n",
+    "5. [Accelerate the Option Pricing Neural Network Model inference with TensorRT](./tensorrt.ipynb)\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/asian_barrier_option/mc_pricing.ipynb b/notebooks/asian_barrier_option/mc_pricing.ipynb
new file mode 100644
index 00000000..d46f01b5
--- /dev/null
+++ b/notebooks/asian_barrier_option/mc_pricing.ipynb
@@ -0,0 +1,900 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Monte Carlo Option pricing using Python libraries\n",
+    "\n",
+    "\n",
+    "Due to the complicated nature of the barrier and price algorithmic averaging, there is no analytical solution for this example of [exotic option](https://www.investopedia.com/terms/e/exoticoption.asp). We can use the Monte Carlo simulation method to estimate the expected value of profit on the maturity day. Traditionally, Monte Carlo pricing is done in the C/C++ CUDA code. In this notebook, we will show this can be done efficiently in the Python libraries like Numba and CuPy.\n",
+    "\n",
+    "Following are the parameters we choose to price the example Asian Barrier Option:\n",
+    "\n",
+    "    Maturity (T): 1 year\n",
+    "    Spot (S) : 120\n",
+    "    Strike (K): 110\n",
+    "    Volatility (sigma): 35.0 %\n",
+    "    Risk Free Rate (r): 5.0 %\n",
+    "    Stock Drift Rate (mu): 10.0 %\n",
+    "    Barrier (B): 100\n",
+    "    \n",
+    "To run this notebook successfully, it is advised to use GPUs with at least 16G memory. V100 GPUs are recommended.\n",
+    "\n",
+    "### CUDA Monte Carlo Option Pricing\n",
+    "\n",
+    "Traditionally, the Monte Caro Option pricing is implemented in CUDA C/C++. Following is one example,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "```C\n",
+       "#include <vector>\n",
+       "#include <stdio.h>\n",
+       "#include <iostream>\n",
+       "#include <chrono>\n",
+       "#include <cuda_runtime.h>\n",
+       "#include <helper_cuda.h>\n",
+       "#include <curand.h>\n",
+       " \n",
+       "#define CHECKCURAND(expression)                         \\\n",
+       "  {                                                     \\\n",
+       "    curandStatus_t status = (expression);                         \\\n",
+       "    if (status != CURAND_STATUS_SUCCESS) {                        \\\n",
+       "      std::cerr << \"Curand Error on line \" << __LINE__<< std::endl;     \\\n",
+       "      std::exit(EXIT_FAILURE);                                          \\\n",
+       "    }                                                                   \\\n",
+       "  }\n",
+       "\n",
+       "// atomicAdd is introduced for compute capability >=6.0\n",
+       "#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 600\n",
+       "#else\n",
+       "__device__ double atomicAdd(double* address, double val)\n",
+       "{\n",
+       "      printf(\"device arch <=600\\n\");\n",
+       "        unsigned long long int* address_as_ull = (unsigned long long int*)address;\n",
+       "          unsigned long long int old = *address_as_ull, assumed;\n",
+       "            do {\n",
+       "                    assumed = old;\n",
+       "                        old = atomicCAS(address_as_ull, assumed,\n",
+       "                                                    __double_as_longlong(val + __longlong_as_double(assumed)));\n",
+       "                          } while (assumed != old);\n",
+       "              return __longlong_as_double(old);\n",
+       "}\n",
+       "#endif\n",
+       "\n",
+       "__global__ void sumPayoffKernel(float *d_s, const unsigned N_PATHS, double *mysum)\n",
+       "{\n",
+       "  unsigned idx =  threadIdx.x + blockIdx.x * blockDim.x;\n",
+       "  unsigned stride = blockDim.x * gridDim.x;\n",
+       "  unsigned tid = threadIdx.x;\n",
+       "\n",
+       "  extern __shared__ double smdata[];\n",
+       "  smdata[tid] = 0.0;\n",
+       "\n",
+       "  for (unsigned i = idx; i<N_PATHS; i+=stride)\n",
+       "  {\n",
+       "    smdata[tid] += (double) d_s[i];\n",
+       "  }\n",
+       "\n",
+       "  for (unsigned s=blockDim.x/2; s>0; s>>=1)\n",
+       "  {\n",
+       "    __syncthreads();\n",
+       "    if (tid < s) smdata[tid] += smdata[tid + s];\n",
+       "  }\n",
+       "\n",
+       "  if (tid == 0)\n",
+       "  {\n",
+       "    atomicAdd(mysum, smdata[0]);\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "__global__ void barrier_option(\n",
+       "    float *d_s,\n",
+       "    const float T,\n",
+       "    const float K,\n",
+       "    const float B,\n",
+       "    const float S0,\n",
+       "    const float sigma,\n",
+       "    const float mu,\n",
+       "    const float r,\n",
+       "    const float * d_normals,\n",
+       "    const long N_STEPS,\n",
+       "    const long N_PATHS)\n",
+       "{\n",
+       "  unsigned idx =  threadIdx.x + blockIdx.x * blockDim.x;\n",
+       "  unsigned stride = blockDim.x * gridDim.x;\n",
+       "  const float tmp1 = mu*T/N_STEPS;\n",
+       "  const float tmp2 = exp(-r*T);\n",
+       "  const float tmp3 = sqrt(T/N_STEPS);\n",
+       "  double running_average = 0.0;\n",
+       "\n",
+       "  for (unsigned i = idx; i<N_PATHS; i+=stride)\n",
+       "  {\n",
+       "    float s_curr = S0;\n",
+       "    for(unsigned n = 0; n < N_STEPS; n++){\n",
+       "       s_curr += tmp1 * s_curr + sigma*s_curr*tmp3*d_normals[i + n * N_PATHS];\n",
+       "       running_average += (s_curr - running_average) / (n + 1.0) ;\n",
+       "       if (running_average <= B){\n",
+       "           break;\n",
+       "       }\n",
+       "    }\n",
+       "\n",
+       "    float payoff = (running_average>K ? running_average-K : 0.f);\n",
+       "    d_s[i] = tmp2 * payoff;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "int main(int argc, char *argv[]) {\n",
+       "  try {\n",
+       "    // declare variables and constants\n",
+       "    size_t N_PATHS = 8192000;\n",
+       "    size_t N_STEPS = 365;\n",
+       "    if (argc >= 2)  N_PATHS = atoi(argv[1]);\n",
+       "\n",
+       "    if (argc >= 3)  N_STEPS = atoi(argv[2]);\n",
+       "\n",
+       "    const float T = 1.0f;\n",
+       "    const float K = 110.0f;\n",
+       "    const float B = 100.0f;\n",
+       "    const float S0 = 120.0f;\n",
+       "    const float sigma = 0.35f;\n",
+       "    const float mu = 0.1f;\n",
+       "    const float r = 0.05f;\n",
+       "\n",
+       "\n",
+       "    double gpu_sum{0.0};\n",
+       "\n",
+       "    int devID{0};\n",
+       "    cudaDeviceProp deviceProps;\n",
+       "\n",
+       "    checkCudaErrors(cudaGetDeviceProperties(&deviceProps, devID));\n",
+       "    printf(\"CUDA device [%s]\\n\", deviceProps.name);\n",
+       "    printf(\"GPU Device %d: \\\"%s\\\" with compute capability %d.%d\\n\\n\", devID, deviceProps.name, deviceProps.major, deviceProps.minor);\n",
+       "    // Generate random numbers on the device\n",
+       "    curandGenerator_t curandGenerator;\n",
+       "    CHECKCURAND(curandCreateGenerator(&curandGenerator, CURAND_RNG_PSEUDO_MTGP32));\n",
+       "    CHECKCURAND(curandSetPseudoRandomGeneratorSeed(curandGenerator, 1234ULL)) ;\n",
+       "\n",
+       "    const size_t N_NORMALS = (size_t)N_STEPS * N_PATHS;\n",
+       "    float *d_normals;\n",
+       "    checkCudaErrors(cudaMalloc(&d_normals, N_NORMALS * sizeof(float)));\n",
+       "    CHECKCURAND(curandGenerateNormal(curandGenerator, d_normals, N_NORMALS, 0.0f, 1.0f));\n",
+       "    cudaDeviceSynchronize();\n",
+       "\n",
+       "  \t// before kernel launch, check the max potential blockSize\n",
+       "  \tint BLOCK_SIZE, GRID_SIZE;\n",
+       "  \tcheckCudaErrors(cudaOccupancyMaxPotentialBlockSize(&GRID_SIZE,\n",
+       "  \t                                                   &BLOCK_SIZE,\n",
+       "  \t                                                   barrier_option,\n",
+       "  \t                                                   0, N_PATHS));\n",
+       "\n",
+       "  \tstd::cout << \"suggested block size \" << BLOCK_SIZE\n",
+       "  \t          << \" \\nsuggested grid size \" << GRID_SIZE\n",
+       "  \t          << std::endl;\n",
+       "\n",
+       "  \tstd::cout << \"Used grid size \" << GRID_SIZE << std::endl;\n",
+       "\n",
+       "  \t// Kernel launch\n",
+       "  \tauto t1=std::chrono::high_resolution_clock::now();\n",
+       "\n",
+       "  \tfloat *d_s;\n",
+       "  \tcheckCudaErrors(cudaMalloc(&d_s, N_PATHS*sizeof(float)));\n",
+       "\n",
+       "  \tauto t3=std::chrono::high_resolution_clock::now();\n",
+       "  \tbarrier_option<<<GRID_SIZE, BLOCK_SIZE>>>(d_s, T, K, B, S0, sigma, mu, r, d_normals, N_STEPS, N_PATHS);\n",
+       "  \tcudaDeviceSynchronize();\n",
+       "  \tauto t4=std::chrono::high_resolution_clock::now();\n",
+       "\n",
+       "  \tdouble* mySum;\n",
+       "  \tcheckCudaErrors(cudaMallocManaged(&mySum, sizeof(double)));\n",
+       "  \tsumPayoffKernel<<<GRID_SIZE, BLOCK_SIZE, BLOCK_SIZE*sizeof(double)>>>(d_s, N_PATHS, mySum);\n",
+       "  \tcudaDeviceSynchronize();\n",
+       "  \tauto t5=std::chrono::high_resolution_clock::now();\n",
+       "\n",
+       "  \tstd::cout << \"sumPayoffKernel takes \"\n",
+       "  \t          << std::chrono::duration_cast<std::chrono::microseconds>(t5-t4).count() / 1000.f\n",
+       "  \t          << \" ms\\n\";\n",
+       "\n",
+       "  \tgpu_sum = mySum[0] / N_PATHS;\n",
+       "\n",
+       "  \tauto t2=std::chrono::high_resolution_clock::now();\n",
+       "\n",
+       "  \t// clean up\n",
+       "  \tCHECKCURAND(curandDestroyGenerator( curandGenerator )) ;\n",
+       "  \tcheckCudaErrors(cudaFree(d_s));\n",
+       "  \tcheckCudaErrors(cudaFree(d_normals));\n",
+       "  \tcheckCudaErrors(cudaFree(mySum));\n",
+       "\n",
+       "  \tstd::cout << \"price \"\n",
+       "              << gpu_sum\n",
+       "              << \" time \"\n",
+       "  \t          << std::chrono::duration_cast<std::chrono::microseconds>(t5-t1).count() / 1000.f\n",
+       "  \t          << \" ms\\n\";\n",
+       "  }\n",
+       "\n",
+       "  catch(std::\n",
+       "        exception& e)\n",
+       "  {\n",
+       "    std::cout<< \"exception: \" << e.what() << \"\\n\";\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "   ```"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Markdown as md\n",
+    "f = open('cuda_pricing.cu', 'r')\n",
+    "md(\"\"\"```C\n",
+    "%s\n",
+    "   ```\"\"\" % (f.read()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The CUDA code is usually long and detailed. In general, it is performing a sequence of 5 tasks:\n",
+    "1. Allocate GPU memory to store the random number and simulation path results\n",
+    "2. Call cuRand library to generate random numbers\n",
+    "3. Launch the barrier option kernel to do parallel simulations\n",
+    "4. Launch the sum kernel to aggregate the terminal derivative prices.\n",
+    "5. Deallocate the memory\n",
+    "\n",
+    "Developers have to perform each step explicitly. \n",
+    "\n",
+    "Compile and run the code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "make: 'out' is up to date.\n",
+      "CUDA device [Tesla V100-SXM2-16GB]\n",
+      "GPU Device 0: \"Tesla V100-SXM2-16GB\" with compute capability 7.0\n",
+      "\n",
+      "suggested block size 1024 \n",
+      "suggested grid size 160\n",
+      "Used grid size 160\n",
+      "sumPayoffKernel takes 1.259 ms\n",
+      "price 18.7026 time 23.123 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "!make out\n",
+    "!./out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compiling and running this CUDA code on a V100 GPU produces the correct option price $18.70$ in $22.05ms$ for $8.192$ million paths and $365$ steps. We will use these numbers as our reference benchmark for later comparison. Among the 5 steps, the critical component is step 3, where data scientists need to describe the detailed Monte Carlo simulation. Ideally the data scientists efforts should be focused on this step. \n",
+    "\n",
+    "## Python Monte Carlo Option Pricing\n",
+    "We set the constants for the option and load the necessary libraries:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cupy\n",
+    "import numpy as np\n",
+    "import math\n",
+    "import time\n",
+    "import numba\n",
+    "from numba import cuda\n",
+    "from numba import njit\n",
+    "from numba import prange\n",
+    "import cudf\n",
+    "cupy.cuda.set_allocator(None)\n",
+    "\n",
+    "N_PATHS = 8192000\n",
+    "N_STEPS = 365\n",
+    "T = 1.0\n",
+    "K = 110.0\n",
+    "B = 100.0\n",
+    "S0 = 120.0\n",
+    "sigma = 0.35\n",
+    "mu = 0.1\n",
+    "r = 0.05"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we know the [Standard Error of the Mean](https://en.wikipedia.org/wiki/Standard_error) is proportional to the inversed square root of the number of samples. Hence the more simulation paths we have, the more accurate the pricing will be. We will simulate $8.192$ million paths with $365$ steps where each step represents a day. \n",
+    "\n",
+    "#### Single Thread CPU\n",
+    "The single thread CPU code for the Monte Carlo simulation has two nested for-loops. The outer loop iterates each path while the inner loop iterates time and computes the underlying asset price for that day. Note that this code is accelerated via [Numba @jit](http://numba.pydata.org/) hence it compiles into machine code at runtime. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@njit(fastmath=True)\n",
+    "def cpu_barrier_option(d_s, T, K, B, S0, sigma, mu, r, d_normals, N_STEPS, N_PATHS):\n",
+    "    tmp1 = mu*T/N_STEPS\n",
+    "    tmp2 = math.exp(-r*T)\n",
+    "    tmp3 = math.sqrt(T/N_STEPS)\n",
+    "    running_average = 0.0\n",
+    "    for i in range(N_PATHS):\n",
+    "        s_curr = S0\n",
+    "        for n in range(N_STEPS):\n",
+    "            s_curr += tmp1 * s_curr + sigma*s_curr*tmp3*d_normals[i + n * N_PATHS]\n",
+    "            running_average = running_average + 1.0/(n + 1.0) * (s_curr - running_average)\n",
+    "            if running_average <= B:\n",
+    "                break\n",
+    "\n",
+    "        payoff = running_average - K if running_average>K else 0\n",
+    "        d_s[i] = tmp2 * payoff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  We use CuPy to generate Gaussian random numbers in the GPU and allocate an array to store the prices at maturity.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "randoms_gpu = cupy.random.normal(0, 1, N_PATHS * N_STEPS, dtype=cupy.float32)\n",
+    "randoms_cpu = np_randoms = cupy.asnumpy(randoms_gpu)\n",
+    "output =  np.zeros(N_PATHS, dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will run the Monte Carlo simulation and time it. When the Numba accelerated function is called for the first time, there is some overhead to compile it. So to time it accurately, we run this method twice and and consider the run time of the second attempt. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time 27.778984785079956 v 18.716661\n"
+     ]
+    }
+   ],
+   "source": [
+    "cpu_barrier_option(output, np.float32(T), np.float32(K), \n",
+    "                    np.float32(B), np.float32(S0), \n",
+    "                    np.float32(sigma), np.float32(mu), \n",
+    "                    np.float32(r), randoms_cpu, N_STEPS, N_PATHS)\n",
+    "s = time.time()\n",
+    "cpu_barrier_option(output, np.float32(T), np.float32(K), \n",
+    "                    np.float32(B), np.float32(S0), \n",
+    "                    np.float32(sigma), np.float32(mu), \n",
+    "                    np.float32(r), randoms_cpu, N_STEPS, N_PATHS)\n",
+    "v = output.mean()\n",
+    "e = time.time()\n",
+    "print('time', e-s, 'v', v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Multiple Cores CPU\n",
+    "CPU has multiple cores and to make a fair comparison, the code can be modified a little to take advantage of all the CPU cores. Note how we parallelize the outer loop:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@njit(fastmath=True, parallel=True)\n",
+    "def cpu_multiplecore_barrier_option(d_s, T, K, B, S0, sigma, mu, r, d_normals, N_STEPS, N_PATHS):\n",
+    "    tmp1 = mu*T/N_STEPS\n",
+    "    tmp2 = math.exp(-r*T)\n",
+    "    tmp3 = math.sqrt(T/N_STEPS)\n",
+    "    for i in prange(N_PATHS):\n",
+    "        s_curr = S0\n",
+    "        running_average = 0.0\n",
+    "        for n in range(N_STEPS):\n",
+    "            s_curr += tmp1 * s_curr + sigma*s_curr*tmp3*d_normals[i + n * N_PATHS]\n",
+    "            running_average = running_average + 1.0/(n + 1.0) * (s_curr - running_average)\n",
+    "            if running_average <= B:\n",
+    "                break\n",
+    "        payoff = running_average - K if running_average>K else 0\n",
+    "        d_s[i] = tmp2 * payoff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Running this parallel code and timing it:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time 1.3648085594177246 v 18.716661\n"
+     ]
+    }
+   ],
+   "source": [
+    "cpu_multiplecore_barrier_option(output, np.float32(T), np.float32(K), \n",
+    "                    np.float32(B), np.float32(S0), \n",
+    "                    np.float32(sigma), np.float32(mu), \n",
+    "                    np.float32(r), randoms_cpu, N_STEPS, N_PATHS)\n",
+    "s = time.time()\n",
+    "cpu_multiplecore_barrier_option(output, np.float32(T), np.float32(K), \n",
+    "                    np.float32(B), np.float32(S0), \n",
+    "                    np.float32(sigma), np.float32(mu), \n",
+    "                    np.float32(r), randoms_cpu, N_STEPS, N_PATHS)\n",
+    "v = output.mean()\n",
+    "e = time.time()\n",
+    "print('time', e-s, 'v', v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see approximately $32x$ speedup due to $32$ cores of the CPU. \n",
+    "\n",
+    "#### NUMBA GPU\n",
+    "The multiple cores CPU code can be modified easily to run in the GPU via Numba.cuda.jit. The code below is very similar to the CPU multiple core code except that we parallelize the outer loop on the GPU. Running this code and timing it:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@cuda.jit\n",
+    "def numba_gpu_barrier_option(d_s, T, K, B, S0, sigma, mu, r, d_normals, N_STEPS, N_PATHS):\n",
+    "    # ii - overall thread index\n",
+    "    ii = cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x\n",
+    "    stride = cuda.gridDim.x * cuda.blockDim.x\n",
+    "    tmp1 = mu*T/N_STEPS\n",
+    "    tmp2 = math.exp(-r*T)\n",
+    "    tmp3 = math.sqrt(T/N_STEPS)\n",
+    "    running_average = 0.0\n",
+    "    for i in range(ii, N_PATHS, stride):\n",
+    "        s_curr = S0\n",
+    "        for n in range(N_STEPS):\n",
+    "            s_curr += tmp1 * s_curr + sigma*s_curr*tmp3*d_normals[i + n * N_PATHS]\n",
+    "            running_average += (s_curr - running_average) / (n + 1.0)\n",
+    "            if running_average <= B:\n",
+    "                break\n",
+    "        payoff = running_average - K if running_average>K else 0\n",
+    "        d_s[i] = tmp2 * payoff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time 0.062163591384887695 v 18.716661\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "number_of_threads = 256\n",
+    "number_of_blocks = (N_PATHS-1) // number_of_threads + 1\n",
+    "output = cupy.zeros(N_PATHS, dtype=cupy.float32)\n",
+    "numba_gpu_barrier_option[(number_of_blocks,), (number_of_threads,)](output, np.float32(T), np.float32(K), \n",
+    "                    np.float32(B), np.float32(S0), \n",
+    "                    np.float32(sigma), np.float32(mu), \n",
+    "                    np.float32(r), randoms_gpu, N_STEPS, N_PATHS)\n",
+    "s = time.time()\n",
+    "numba_gpu_barrier_option[(number_of_blocks,), (number_of_threads,)](output, np.float32(T), np.float32(K), \n",
+    "                    np.float32(B), np.float32(S0), \n",
+    "                    np.float32(sigma), np.float32(mu), \n",
+    "                    np.float32(r), randoms_gpu, N_STEPS, N_PATHS)\n",
+    "v = output.mean()\n",
+    "cuda.synchronize()\n",
+    "e = time.time()\n",
+    "print('time', e-s, 'v', v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We get $4x$ speedup compared to the multiple cores version and $128x$ speedup compared to the single core version. \n",
+    "\n",
+    "#### NUMBA Shared Memory \n",
+    "While accessing the global memory for Gaussian random numbers, the memory access is already aligned and numbers are only read once. So using shared memory is not helping the performance as shown below:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@cuda.jit\n",
+    "def numba_gpu_barrier_option_shared_mem(d_s, T, K, B, S0, sigma, mu, r, d_normals, N_STEPS, N_PATHS):\n",
+    "    shared = cuda.shared.array(shape=0, dtype=numba.float32)\n",
+    "    # load to shared memory\n",
+    "    path_offset = cuda.blockIdx.x * cuda.blockDim.x\n",
+    "    ii = cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x\n",
+    "    stride = cuda.gridDim.x * cuda.blockDim.x\n",
+    "    tmp1 = mu*T/N_STEPS\n",
+    "    tmp2 = math.exp(-r*T)\n",
+    "    tmp3 = math.sqrt(T/N_STEPS)\n",
+    "    running_average = 0.0\n",
+    "    for i in range(ii, N_PATHS, stride):\n",
+    "        s_curr = S0\n",
+    "        for n in range(N_STEPS):\n",
+    "            shared[cuda.threadIdx.x] = d_normals[path_offset + cuda.threadIdx.x + n * N_PATHS]\n",
+    "            s_curr += tmp1 * s_curr + sigma*s_curr*tmp3*shared[cuda.threadIdx.x]\n",
+    "            running_average += (s_curr - running_average) / (n + 1.0)\n",
+    "            if running_average <= B:\n",
+    "                break\n",
+    "        payoff = running_average - K if running_average>K else 0\n",
+    "        d_s[i] = tmp2 * payoff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time 0.06269669532775879 v 18.716661\n"
+     ]
+    }
+   ],
+   "source": [
+    "number_of_threads = 256\n",
+    "number_of_blocks = (N_PATHS-1) // number_of_threads + 1\n",
+    "output = cupy.zeros(N_PATHS, dtype=cupy.float32)\n",
+    "shared_buffer_size = number_of_threads * 4\n",
+    "numba_gpu_barrier_option_shared_mem[(number_of_blocks,), (number_of_threads,), 0, shared_buffer_size](output, np.float32(T), np.float32(K), \n",
+    "                    np.float32(B), np.float32(S0), \n",
+    "                    np.float32(sigma), np.float32(mu), \n",
+    "                    np.float32(r), randoms_gpu, N_STEPS, N_PATHS)\n",
+    "s = time.time()\n",
+    "numba_gpu_barrier_option_shared_mem[(number_of_blocks,), (number_of_threads,), 0, shared_buffer_size](output, np.float32(T), np.float32(K), \n",
+    "                    np.float32(B), np.float32(S0), \n",
+    "                    np.float32(sigma), np.float32(mu), \n",
+    "                    np.float32(r), randoms_gpu, N_STEPS, N_PATHS)\n",
+    "v = output.mean()\n",
+    "cuda.synchronize()\n",
+    "e = time.time()\n",
+    "print('time', e-s, 'v', v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CUPY GPU\n",
+    "CuPy provides an easy way to define GPU kernels from raw CUDA source. `RawKernel` object allows you to call the kernel with CUDA’s `cuLaunchKernel` interface. Here is an example where we wrap the Barrier Option computation code inside the `RawKernel`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cupy_barrier_option = cupy.RawKernel(r'''\n",
+    "extern \"C\" __global__ void barrier_option(\n",
+    "    float *d_s,\n",
+    "    const float T,\n",
+    "    const float K,\n",
+    "    const float B,\n",
+    "    const float S0,\n",
+    "    const float sigma,\n",
+    "    const float mu,\n",
+    "    const float r,\n",
+    "    const float * d_normals,\n",
+    "    const long N_STEPS,\n",
+    "    const long N_PATHS)\n",
+    "{\n",
+    "  unsigned idx =  threadIdx.x + blockIdx.x * blockDim.x;\n",
+    "  unsigned stride = blockDim.x * gridDim.x;\n",
+    "  unsigned tid = threadIdx.x;\n",
+    "\n",
+    "  const float tmp1 = mu*T/N_STEPS;\n",
+    "  const float tmp2 = exp(-r*T);\n",
+    "  const float tmp3 = sqrt(T/N_STEPS);\n",
+    "  double running_average = 0.0;\n",
+    "\n",
+    "  for (unsigned i = idx; i<N_PATHS; i+=stride)\n",
+    "  {\n",
+    "    float s_curr = S0;\n",
+    "    unsigned n=0;\n",
+    "    for(unsigned n = 0; n < N_STEPS; n++){\n",
+    "       s_curr += tmp1 * s_curr + sigma*s_curr*tmp3*d_normals[i + n * N_PATHS];\n",
+    "       running_average += (s_curr - running_average) / (n + 1.0) ;\n",
+    "       if (running_average <= B){\n",
+    "           break;\n",
+    "       }\n",
+    "    }\n",
+    "\n",
+    "    float payoff = (running_average>K ? running_average-K : 0.f);\n",
+    "    d_s[i] = tmp2 * payoff;\n",
+    "  }\n",
+    "}\n",
+    "\n",
+    "''', 'barrier_option')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can launch it to compute the same Barrier Option price:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time 0.025580167770385742 v 18.716661\n"
+     ]
+    }
+   ],
+   "source": [
+    "number_of_threads = 256\n",
+    "number_of_blocks = (N_PATHS-1) // number_of_threads + 1\n",
+    "s = time.time()\n",
+    "cupy_barrier_option((number_of_blocks,), (number_of_threads,),\n",
+    "                   (output, np.float32(T), np.float32(K), \n",
+    "                    np.float32(B), np.float32(S0), \n",
+    "                    np.float32(sigma), np.float32(mu), \n",
+    "                    np.float32(r),  randoms_gpu, N_STEPS, N_PATHS))\n",
+    "v = output.mean()\n",
+    "cupy.cuda.stream.get_current_stream().synchronize()\n",
+    "e = time.time()\n",
+    "print('time', e-s, 'v',v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This approach is the most efficient way to use the GPU and it achieves 8x speedup compared to the 32 core CPU performance. Compared with CUDA C/C++ approach ($23ms$), CuPy performance ($25ms$) is very close to it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multiple GPUs Option Pricing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get a more accurate estimation of the option price, more paths are needed for Monte Carlo simulation. The single V100 GPU we used in the above example only has 32GB memory and we are hitting the memory limits to run 8M simulations. [DASK](https://dask.org/) is an integrated component of RAPIDS for distributed computation on GPUs.  We can take advantage of it to distribute the Monte Carlo simulation computation to multiple nodes across multiple GPUs. First, we need to wrap all the computation inside a function to allow the allocated GPU memory to be released at the end of the function call. Note that the function takes an extra argument for the random number seed value so the individual function calls each have an independent sequence of random numbers. Loading the DASK library and setting up the local CUDA cluster :-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear the GPU memory\n",
+    "del randoms_gpu \n",
+    "del randoms_cpu\n",
+    "del output\n",
+    "\n",
+    "\n",
+    "def get_option_price(T, K, B, S0, sigma, mu, r, N_PATHS = 8192000, N_STEPS = 365, seed=3):\n",
+    "    number_of_threads = 256\n",
+    "    number_of_blocks = (N_PATHS-1) // number_of_threads + 1\n",
+    "    cupy.random.seed(seed)\n",
+    "    randoms_gpu = cupy.random.normal(0, 1, N_PATHS * N_STEPS, dtype=cupy.float32)\n",
+    "    output =  cupy.zeros(N_PATHS, dtype=cupy.float32)\n",
+    "    cupy_barrier_option((number_of_blocks,), (number_of_threads,),\n",
+    "                   (output, np.float32(T), np.float32(K), \n",
+    "                    np.float32(B), np.float32(S0), \n",
+    "                    np.float32(sigma), np.float32(mu), \n",
+    "                    np.float32(r),  randoms_gpu, N_STEPS, N_PATHS))\n",
+    "    v = output.mean()\n",
+    "    out_df = cudf.DataFrame()\n",
+    "    out_df['p'] = cudf.Series([v.item()])\n",
+    "    return out_df\n",
+    "o = get_option_price(T=1.0, K=120.0, B=90.0, S0=100.0, sigma=0.2, mu=0.1, r=0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Client</h3>\n",
+       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:34615</li>\n",
+       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
+       "</ul>\n",
+       "</td>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
+       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
+       "  <li><b>Workers: </b>8</li>\n",
+       "  <li><b>Cores: </b>8</li>\n",
+       "  <li><b>Memory: </b>540.94 GB</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: 'tcp://127.0.0.1:34615' processes=8 threads=8, memory=540.94 GB>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import dask\n",
+    "import dask_cudf\n",
+    "from dask.delayed import delayed\n",
+    "from dask_cuda import LocalCUDACluster\n",
+    "cluster = LocalCUDACluster()\n",
+    "from dask.distributed import Client\n",
+    "client = Client(cluster)\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are 4 GPUs inside the system. To distribute the above function, we wrap it into the `delayed` function to integrate it into the DASK computation graph. We use `from_delayed` to gather all the distributed dataframes into a holistic cudf_dask dataframe. We can call the cudf_dask dataframe `mean` and `std` to calculate the expected mean and standard deviation of the prices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = dask_cudf.from_delayed([delayed(get_option_price)(T=1.0, K=110.0, B=100.0, S0=120.0, sigma=0.35, mu=0.1, r=0.05, seed=3000+i) for i in range(1600)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "p    18.711432\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.mean().compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "p    0.007374\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.std().compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The code computed 1600 Monte Carlo simulations of `8192000` paths. By averaging the price together to get a better estimation, the standard deviation is reduced by a factor of 1/sqrt(1600) = 1/40  "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/asian_barrier_option/tensorrt.ipynb b/notebooks/asian_barrier_option/tensorrt.ipynb
new file mode 100644
index 00000000..d921317e
--- /dev/null
+++ b/notebooks/asian_barrier_option/tensorrt.ipynb
@@ -0,0 +1,532 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TensorRT Inference\n",
+    "\n",
+    "After training the deep learning network, the next step is to usually deploy the model to production. The most straight-forward way is to put the PyTorch model in inference mode. The model below loads the trained weights from the PyTorch check point file and sets the weights of the deep learning model. The inference is to do a forward pass from input to the output. We can see it runs fairly quickly to get accurate results in less than 1ms. Here is an example from the last notebook:- "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import time\n",
+    "class Net(nn.Module):\n",
+    "\n",
+    "    def __init__(self, hidden=512):\n",
+    "        super(Net, self).__init__()\n",
+    "        self.fc1 = nn.Linear(6, hidden)\n",
+    "        self.fc2 = nn.Linear(hidden, hidden)\n",
+    "        self.fc3 = nn.Linear(hidden, hidden)\n",
+    "        self.fc4 = nn.Linear(hidden, hidden)\n",
+    "        self.fc5 = nn.Linear(hidden, hidden)\n",
+    "        self.fc6 = nn.Linear(hidden, 1)\n",
+    "        self.register_buffer('norm',\n",
+    "                             torch.tensor([200.0,\n",
+    "                                           198.0,\n",
+    "                                           200.0,\n",
+    "                                           0.4,\n",
+    "                                           0.2,\n",
+    "                                           0.2]))\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = x / self.norm\n",
+    "        x = F.elu(self.fc1(x))\n",
+    "        x = F.elu(self.fc2(x))\n",
+    "        x = F.elu(self.fc3(x))\n",
+    "        x = F.elu(self.fc4(x))\n",
+    "        x = F.elu(self.fc5(x))\n",
+    "        return self.fc6(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset is already present. No need to re-download it.\n"
+     ]
+    }
+   ],
+   "source": [
+    "! ((test ! -f './check_points/model_best.pth.tar' ||  test ! -f './check_points/512/model_best.pth.tar') && \\\n",
+    "  bash ./download_data.sh) || echo \"Dataset is already present. No need to re-download it.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "result 18.6810 inference time 0.184153\n"
+     ]
+    }
+   ],
+   "source": [
+    "checkpoint = torch.load('check_points/512/model_best.pth.tar')\n",
+    "model = Net().cuda()\n",
+    "model.load_state_dict(checkpoint['state_dict'])\n",
+    "inputs = torch.tensor([[110.0, 100.0, 120.0, 0.35, 0.1, 0.05]])\n",
+    "start = time.time()\n",
+    "inputs = inputs.cuda()\n",
+    "result = model(inputs)\n",
+    "end = time.time()\n",
+    "print('result %.4f inference time %.6f' % (result,end- start))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, we can do much better. NVIDIA provides a powerful inference model optimization tool [TensorRT](https://developer.nvidia.com/tensorrt) which includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications. It made NVIDIA win the [MLPerf Inference benchmark](https://devblogs.nvidia.com/nvidia-mlperf-v05-ai-inference/).  In this [blog](https://devblogs.nvidia.com/nlu-with-tensorrt-bert/#disqus_thread), TensorRT helps to accelerate the BERT natural language understanding inference to 2.2ms on the T4 GPU. \n",
+    "\n",
+    "In this notebook inspired by the [BERT inference blog](https://devblogs.nvidia.com/nlu-with-tensorrt-bert/#disqus_thread) we will demonstrate step-by-step, how we can convert the trained Asian Barrier Option model to TensorRT inference engine to get significant acceleration. \n",
+    "\n",
+    "Our network is a simple feed-forward fully connected network with `Elu` activation function. `Elu` is not directly supported by TensorRT yet. We will show how to customize the activation function in CUDA.\n",
+    "\n",
+    "From PyTorch document, we can find the math formulae of `ELU` activation function.\n",
+    "```\n",
+    "ELU(x)=max(0,x)+min(0,α∗(exp(x)−1))\n",
+    "```\n",
+    "\n",
+    "This can be translated into CUDA code as:-\n",
+    "```c++\n",
+    "template <typename T, unsigned TPB>\n",
+    "__global__ void eluKernel(const T a, const T b, int n, const T* input, T* output)\n",
+    "{\n",
+    "\n",
+    "    const int idx = blockIdx.x * TPB + threadIdx.x;\n",
+    "\n",
+    "    if (idx < n)\n",
+    "    {\n",
+    "        const T in = input[idx];\n",
+    "        const T tmp = exp(in) - b;\n",
+    "        const T result = (a > in ? a : in) + (a < tmp ? a : tmp);\n",
+    "        output[idx] = result;\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "```\n",
+    "\n",
+    "where `a` is a constant 0 and `b` is a constant 1. We set them into variables of type `T` so that we can handle single precision or half precision inferences by TensorRT. We follow the examples described in [BERT inference blog](https://devblogs.nvidia.com/nlu-with-tensorrt-bert/#disqus_thread), and wrap the CUDA kernel in `EluPluginDynamic` which is a subclass of `nvinfer1::IPluginV2DynamicExt`.\n",
+    "\n",
+    "Run the following command to build the plugins into dynamic libraries:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mkdir -p elu_activation/build"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Errno 2] No such file or directory: 'elu_activation/build'\n",
+      "/Projects/gQuant/notebooks/asian_barrier_option/elu_activation/build\n"
+     ]
+    }
+   ],
+   "source": [
+    "cd elu_activation/build"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-- The CXX compiler identification is GNU 7.4.0\n",
+      "-- The CUDA compiler identification is NVIDIA 10.1.243\n",
+      "-- Check for working CXX compiler: /usr/bin/c++\n",
+      "-- Check for working CXX compiler: /usr/bin/c++ -- works\n",
+      "-- Detecting CXX compiler ABI info\n",
+      "-- Detecting CXX compiler ABI info - done\n",
+      "-- Detecting CXX compile features\n",
+      "-- Detecting CXX compile features - done\n",
+      "-- Check for working CUDA compiler: /usr/local/cuda/bin/nvcc\n",
+      "-- Check for working CUDA compiler: /usr/local/cuda/bin/nvcc -- works\n",
+      "-- Detecting CUDA compiler ABI info\n",
+      "-- Detecting CUDA compiler ABI info - done\n",
+      "-- Configuring done\n",
+      "-- Generating done\n",
+      "-- Build files have been written to: /Projects/gQuant/notebooks/asian_barrier_option/elu_activation/build\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cmake ../"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[35m\u001b[1mScanning dependencies of target common\u001b[0m\n",
+      "[ 20%] \u001b[32mBuilding CXX object CMakeFiles/common.dir/log/logger.cpp.o\u001b[0m\n",
+      "[ 40%] \u001b[32m\u001b[1mLinking CXX shared library libcommon.so\u001b[0m\n",
+      "[ 40%] Built target common\n",
+      "\u001b[35m\u001b[1mScanning dependencies of target my_plugins\u001b[0m\n",
+      "[ 60%] \u001b[32mBuilding CUDA object CMakeFiles/my_plugins.dir/plugins/eluPlugin.cu.o\u001b[0m\n",
+      "/Projects/gQuant/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h(44): warning: function \"nvinfer1::IPluginV2::getOutputDimensions(int, const nvinfer1::Dims *, int)\" is hidden by \"elu::EluPluginDynamic::getOutputDimensions\" -- virtual function override intended?\n",
+      "\n",
+      "/Projects/gQuant/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h(48): warning: function \"nvinfer1::IPluginV2Ext::configurePlugin(const nvinfer1::Dims *, int, const nvinfer1::Dims *, int, const nvinfer1::DataType *, const nvinfer1::DataType *, const __nv_bool *, const __nv_bool *, nvinfer1::PluginFormat, int)\" is hidden by \"elu::EluPluginDynamic::configurePlugin\" -- virtual function override intended?\n",
+      "\n",
+      "/Projects/gQuant/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h(50): warning: function \"nvinfer1::IPluginV2::getWorkspaceSize(int) const\" is hidden by \"elu::EluPluginDynamic::getWorkspaceSize\" -- virtual function override intended?\n",
+      "\n",
+      "/Projects/gQuant/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h(52): warning: function \"nvinfer1::IPluginV2::enqueue(int, const void *const *, void **, void *, cudaStream_t)\" is hidden by \"elu::EluPluginDynamic::enqueue\" -- virtual function override intended?\n",
+      "\n",
+      "/Projects/gQuant/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h(44): warning: function \"nvinfer1::IPluginV2::getOutputDimensions(int, const nvinfer1::Dims *, int)\" is hidden by \"elu::EluPluginDynamic::getOutputDimensions\" -- virtual function override intended?\n",
+      "\n",
+      "/Projects/gQuant/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h(48): warning: function \"nvinfer1::IPluginV2Ext::configurePlugin(const nvinfer1::Dims *, int, const nvinfer1::Dims *, int, const nvinfer1::DataType *, const nvinfer1::DataType *, const __nv_bool *, const __nv_bool *, nvinfer1::PluginFormat, int)\" is hidden by \"elu::EluPluginDynamic::configurePlugin\" -- virtual function override intended?\n",
+      "\n",
+      "/Projects/gQuant/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h(50): warning: function \"nvinfer1::IPluginV2::getWorkspaceSize(int) const\" is hidden by \"elu::EluPluginDynamic::getWorkspaceSize\" -- virtual function override intended?\n",
+      "\n",
+      "/Projects/gQuant/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h(52): warning: function \"nvinfer1::IPluginV2::enqueue(int, const void *const *, void **, void *, cudaStream_t)\" is hidden by \"elu::EluPluginDynamic::enqueue\" -- virtual function override intended?\n",
+      "\n",
+      "/Projects/gQuant/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h(44): warning: function \"nvinfer1::IPluginV2::getOutputDimensions(int, const nvinfer1::Dims *, int)\" is hidden by \"elu::EluPluginDynamic::getOutputDimensions\" -- virtual function override intended?\n",
+      "\n",
+      "/Projects/gQuant/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h(48): warning: function \"nvinfer1::IPluginV2Ext::configurePlugin(const nvinfer1::Dims *, int, const nvinfer1::Dims *, int, const nvinfer1::DataType *, const nvinfer1::DataType *, const bool *, const bool *, nvinfer1::PluginFormat, int)\" is hidden by \"elu::EluPluginDynamic::configurePlugin\" -- virtual function override intended?\n",
+      "\n",
+      "/Projects/gQuant/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h(50): warning: function \"nvinfer1::IPluginV2::getWorkspaceSize(int) const\" is hidden by \"elu::EluPluginDynamic::getWorkspaceSize\" -- virtual function override intended?\n",
+      "\n",
+      "/Projects/gQuant/notebooks/asian_barrier_option/elu_activation/plugins/eluPlugin.h(52): warning: function \"nvinfer1::IPluginV2::enqueue(int, const void *const *, void **, void *, cudaStream_t)\" is hidden by \"elu::EluPluginDynamic::enqueue\" -- virtual function override intended?\n",
+      "\n",
+      "[ 80%] \u001b[32m\u001b[1mLinking CUDA device code CMakeFiles/my_plugins.dir/cmake_device_link.o\u001b[0m\n",
+      "[100%] \u001b[32m\u001b[1mLinking CUDA shared library libmy_plugins.so\u001b[0m\n",
+      "[100%] Built target my_plugins\n"
+     ]
+    }
+   ],
+   "source": [
+    "!make -j"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Projects/gQuant/notebooks/asian_barrier_option\n"
+     ]
+    }
+   ],
+   "source": [
+    "cd ../../"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use ctypes to load those dynamic libraries and register them in tensorRT:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorrt as trt\n",
+    "import ctypes\n",
+    "import numpy as np\n",
+    "TRT_LOGGER = trt.Logger(trt.Logger.INFO)\n",
+    "ctypes.CDLL(\"libnvinfer_plugin.so\", mode=ctypes.RTLD_GLOBAL)\n",
+    "ctypes.CDLL(\"elu_activation/build/libcommon.so\", mode=ctypes.RTLD_GLOBAL)\n",
+    "ctypes.CDLL(\"elu_activation/build/libmy_plugins.so\", mode=ctypes.RTLD_GLOBAL)\n",
+    "trt.init_libnvinfer_plugins(TRT_LOGGER, \"\")\n",
+    "plg_registry = trt.get_plugin_registry()\n",
+    "elu_plg_creator = plg_registry.get_plugin_creator(\"CustomEluPluginDynamic\", \"1\", \"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next step is to convert the PyTorch check point weights into TensorRT weights:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_trt_weights(model_dict):\n",
+    "    weight_dict = dict()\n",
+    "    for k in model_dict.keys():\n",
+    "        if k.find('weight') >= 0:\n",
+    "            weight_dict[k] = trt.Weights(model_dict[k].cpu().numpy())\n",
+    "        else:\n",
+    "            weight_dict[k] = trt.Weights(model_dict[k].cpu().numpy())\n",
+    "    return weight_dict\n",
+    "weights = get_trt_weights(checkpoint['state_dict'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can check that the weights have the following weight keys corresponding to each of the layers in the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['norm', 'fc1.weight', 'fc1.bias', 'fc2.weight', 'fc2.bias', 'fc3.weight', 'fc3.bias', 'fc4.weight', 'fc4.bias', 'fc5.weight', 'fc5.bias', 'fc6.weight', 'fc6.bias'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(weights.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To build the TensorRT engine, we need the network to be defined. There are two ways of doing this. We can either use the network parser which can convert the TensorFlow static graph or Onnx graph into the TensorRT network directly, or we can use the Network API to define the network. In this example, we will show the latter approach.\n",
+    "\n",
+    "From the Pytorch model, we see the first step is to normalize the input to the range [0-1]. In TensorRT, it can be done by:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalize_layer(network, weights, inputs):\n",
+    "    # the constant layer to load the normalization factor\n",
+    "    const = network.add_constant((1, 6, 1, 1), weights['norm'])\n",
+    "    output = network.add_elementwise(inputs, const.get_output(0), trt.ElementWiseOperation.DIV)  \n",
+    "    out_tensor = output.get_output(0)\n",
+    "    return out_tensor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the normalization, the input will be projected to a `hidden` dimension and applied to `Elu` activation, this can be done by:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def projection_activation(network, weights, inputs, lid):\n",
+    "    layer = network.add_fully_connected(inputs, hidden, weights['fc'+str(lid)+'.weight'], weights['fc'+str(lid)+'.bias'])    \n",
+    "    pfc = trt.PluginFieldCollection()\n",
+    "    plug = elu_plg_creator.create_plugin(\"elu\", pfc)\n",
+    "    elu_layer = network.add_plugin_v2([layer.get_output(0)], plug)\n",
+    "    out_tensor = elu_layer.get_output(0)\n",
+    "    out_tensor.name = 'l'+str(lid)+'elu'\n",
+    "    return out_tensor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Following is the code to build the full network, run optimization to get the TensorRT engine and serialize it to the file `opt.engine`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hidden=512\n",
+    "with trt.Builder(TRT_LOGGER) as builder:\n",
+    "    explicit_batch_flag = 1\n",
+    "    with builder.create_network(explicit_batch_flag) as network, builder.create_builder_config() as builder_config:\n",
+    "        builder_config.max_workspace_size = 5000 * (1024 * 1024)\n",
+    "        builder_config.set_flag(trt.BuilderFlag.FP16)\n",
+    "        # inputs has to be of shape (B, C, H, W) so we can use fully connected layer\n",
+    "        inputs = network.add_input(name=\"option_para\", dtype=trt.float32, shape=(-1, 6, 1, 1))\n",
+    "        # create one profile that handles batch size 1\n",
+    "        bs1_profile = builder.create_optimization_profile()\n",
+    "        shape = (1, 6, 1, 1)\n",
+    "        bs1_profile.set_shape(\"option_para\", min=shape, opt=shape, max=shape)\n",
+    "        # create another profile that handles batch size 8\n",
+    "        bs8_profile = builder.create_optimization_profile()\n",
+    "        shape = (8, 6, 1, 1)\n",
+    "        bs8_profile.set_shape(\"option_para\", min=shape, opt=shape, max=shape)        \n",
+    "        builder_config.add_optimization_profile(bs1_profile)\n",
+    "        builder_config.add_optimization_profile(bs8_profile)\n",
+    "        \n",
+    "        # normalize the input to range 0-1\n",
+    "        out_tensor = normalize_layer(network, weights, inputs) \n",
+    "        \n",
+    "        # project it to hidden dimension 512 and apply Elu activation 5 times\n",
+    "        out_tensor = projection_activation(network, weights, out_tensor, 1)\n",
+    "        out_tensor = projection_activation(network, weights, out_tensor, 2)\n",
+    "        out_tensor = projection_activation(network, weights, out_tensor, 3)\n",
+    "        out_tensor = projection_activation(network, weights, out_tensor, 4)\n",
+    "        out_tensor = projection_activation(network, weights, out_tensor, 5)\n",
+    "        \n",
+    "        # project it to dimension 1 to get the price\n",
+    "        layer = network.add_fully_connected(out_tensor, 1, weights['fc6.weight'], weights['fc6.bias'])\n",
+    "        out_tensor = layer.get_output(0)\n",
+    "        out_tensor.name = 'output'\n",
+    "        # mark the output tensor\n",
+    "        network.mark_output(out_tensor)\n",
+    "        \n",
+    "        # run optimization to find the best plan\n",
+    "        engine = builder.build_engine(network, builder_config)\n",
+    "        # serialize the model into file\n",
+    "        serialized_engine = engine.serialize()\n",
+    "        with open('opt.engine', 'wb') as fout:\n",
+    "            fout.write(serialized_engine)\n",
+    "        TRT_LOGGER.log(TRT_LOGGER.INFO, \"Done.\")\n",
+    "            \n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have the TensorRT engine file ready, it is easy to use it for inference work. We need to:-\n",
+    "1. Load the serialized engine file\n",
+    "2. Allocate the CUDA device array\n",
+    "3. Async copy input from host to device\n",
+    "4. Launch the TensorRT engine to compute the result\n",
+    "5. Async copy the output from device to host"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "result 18.6810 inference time 0.000201\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorrt as trt\n",
+    "import time\n",
+    "import numpy as np\n",
+    "import pycuda\n",
+    "import pycuda.driver as cuda\n",
+    "import pycuda.autoinit\n",
+    "\n",
+    "TRT_LOGGER = trt.Logger(trt.Logger.WARNING)\n",
+    "\n",
+    "with open(\"opt.engine\", \"rb\") as f, trt.Runtime(TRT_LOGGER) as runtime:\n",
+    "    engine = runtime.deserialize_cuda_engine(f.read())\n",
+    "\n",
+    "h_input = cuda.pagelocked_empty((1,6,1,1), dtype=np.float32)\n",
+    "h_input[0, 0, 0, 0] = 110.0\n",
+    "h_input[0, 1, 0, 0] = 100.0\n",
+    "h_input[0, 2, 0, 0] = 120.0\n",
+    "h_input[0, 3, 0, 0] = 0.35\n",
+    "h_input[0, 4, 0, 0] = 0.1\n",
+    "h_input[0, 5, 0, 0] = 0.05\n",
+    "h_output = cuda.pagelocked_empty((1,1,1,1), dtype=np.float32)\n",
+    "d_input = cuda.mem_alloc(h_input.nbytes)\n",
+    "d_output = cuda.mem_alloc(h_output.nbytes)\n",
+    "stream = cuda.Stream()\n",
+    "with engine.create_execution_context() as context:\n",
+    "    start = time.time()\n",
+    "    cuda.memcpy_htod_async(d_input, h_input, stream)\n",
+    "    input_shape = (1, 6, 1, 1)\n",
+    "    context.set_binding_shape(0, input_shape)\n",
+    "    context.execute_async(bindings=[int(d_input), int(d_output)], stream_handle=stream.handle)\n",
+    "    cuda.memcpy_dtoh_async(h_output, d_output, stream)\n",
+    "    stream.synchronize()\n",
+    "    end = time.time()\n",
+    "print('result %.4f inference time %.6f' % (h_output,end- start))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It produces accurate result in half of the inference time compared to the non TensorRT approach"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 27ae2c9afdb2a7f61bc752f7207b5b350fa00c6c Mon Sep 17 00:00:00 2001
From: doyend <doyend@gmail.com>
Date: Mon, 20 Apr 2020 19:04:40 -0400
Subject: [PATCH 5/6] fixed the gamma computation error (#79)

* fixed the gamma computation error
* add grad example
---
 .../deep_learning_option_2.ipynb              | 152 ++++++++++++------
 1 file changed, 100 insertions(+), 52 deletions(-)

diff --git a/notebooks/asian_barrier_option/deep_learning_option_2.ipynb b/notebooks/asian_barrier_option/deep_learning_option_2.ipynb
index fee3afcf..c4991256 100644
--- a/notebooks/asian_barrier_option/deep_learning_option_2.ipynb
+++ b/notebooks/asian_barrier_option/deep_learning_option_2.ipynb
@@ -701,45 +701,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "--2020-03-02 19:50:47--  https://query.data.world/s/fb3ilrt77qcpx7kwnfgr3cybvdctk2\n",
-      "Resolving query.data.world (query.data.world)... 3.222.149.11, 54.86.110.27, 54.85.70.45\n",
-      "Connecting to query.data.world (query.data.world)|3.222.149.11|:443... connected.\n",
-      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
-      "Location: https://download.data.world/file_download/yidata/weights1024/model_best.pth.tar?auth=eyJhbGciOiJIUzUxMiJ9.eyJzdWIiOiJwcm9kLXVzZXItY2xpZW50OnlpZGF0YSIsImlzcyI6ImFnZW50OnlpZGF0YTo6ZjJkYTQ1NmUtYjA5MC00ZjdiLTgyNTYtOWU0ZTFjNTA5ZGRmIiwiaWF0IjoxNTgxNzAzMDY5LCJyb2xlIjpbInVzZXIiLCJ1c2VyX2FwaV9hZG1pbiIsInVzZXJfYXBpX3JlYWQiLCJ1c2VyX2FwaV93cml0ZSJdLCJnZW5lcmFsLXB1cnBvc2UiOmZhbHNlLCJ1cmwiOiJiZTQxOTdiNDQ2OTZjOWFjNmRjNmFlMDZjMzQxZGE5ZmI2MTY4ODZhIn0.ivgs7kPSxaf1EkRk_CfBrH8BNquYpkiFHnFDOAiY4_9MxfluMFREgtmUUftiYD7536Y6PsNC-x62FrtoZC4JXA [following]\n",
-      "--2020-03-02 19:50:47--  https://download.data.world/file_download/yidata/weights1024/model_best.pth.tar?auth=eyJhbGciOiJIUzUxMiJ9.eyJzdWIiOiJwcm9kLXVzZXItY2xpZW50OnlpZGF0YSIsImlzcyI6ImFnZW50OnlpZGF0YTo6ZjJkYTQ1NmUtYjA5MC00ZjdiLTgyNTYtOWU0ZTFjNTA5ZGRmIiwiaWF0IjoxNTgxNzAzMDY5LCJyb2xlIjpbInVzZXIiLCJ1c2VyX2FwaV9hZG1pbiIsInVzZXJfYXBpX3JlYWQiLCJ1c2VyX2FwaV93cml0ZSJdLCJnZW5lcmFsLXB1cnBvc2UiOmZhbHNlLCJ1cmwiOiJiZTQxOTdiNDQ2OTZjOWFjNmRjNmFlMDZjMzQxZGE5ZmI2MTY4ODZhIn0.ivgs7kPSxaf1EkRk_CfBrH8BNquYpkiFHnFDOAiY4_9MxfluMFREgtmUUftiYD7536Y6PsNC-x62FrtoZC4JXA\n",
-      "Resolving download.data.world (download.data.world)... 54.85.70.45, 54.86.110.27, 3.222.149.11\n",
-      "Connecting to download.data.world (download.data.world)|54.85.70.45|:443... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: unspecified [application/x-tar]\n",
-      "Saving to: ‘./check_points/model_best.pth.tar’\n",
-      "\n",
-      "./check_points/mode     [         <=>        ]  48.15M  29.9MB/s    in 1.6s    \n",
-      "\n",
-      "2020-03-02 19:50:51 (29.9 MB/s) - ‘./check_points/model_best.pth.tar’ saved [50484852]\n",
-      "\n",
-      "--2020-03-02 19:50:51--  https://query.data.world/s/o2kzs74pg22mc2mfyhkykyu6pq36yr\n",
-      "Resolving query.data.world (query.data.world)... 3.222.149.11, 54.86.110.27, 54.85.70.45\n",
-      "Connecting to query.data.world (query.data.world)|3.222.149.11|:443... connected.\n",
-      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
-      "Location: https://download.data.world/file_download/yidata/weight512/model_best.pth.tar?auth=eyJhbGciOiJIUzUxMiJ9.eyJzdWIiOiJwcm9kLXVzZXItY2xpZW50OnlpZGF0YSIsImlzcyI6ImFnZW50OnlpZGF0YTo6ZjJkYTQ1NmUtYjA5MC00ZjdiLTgyNTYtOWU0ZTFjNTA5ZGRmIiwiaWF0IjoxNTgxNzAzMTU2LCJyb2xlIjpbInVzZXIiLCJ1c2VyX2FwaV9hZG1pbiIsInVzZXJfYXBpX3JlYWQiLCJ1c2VyX2FwaV93cml0ZSJdLCJnZW5lcmFsLXB1cnBvc2UiOmZhbHNlLCJ1cmwiOiIxYTMyMjY4YzA4YjMzYzJiMzlhMjg5MTA4NDE5OGFiZjNjZWExNzdmIn0.QEUxrUZ0uyXu2-cLU6JrhmNHWwScObX0NYghH8UdLP8SJXA6AefVZrtRBINeK6j_iM8ibOzJ19FidH1r5BsJbA [following]\n",
-      "--2020-03-02 19:50:51--  https://download.data.world/file_download/yidata/weight512/model_best.pth.tar?auth=eyJhbGciOiJIUzUxMiJ9.eyJzdWIiOiJwcm9kLXVzZXItY2xpZW50OnlpZGF0YSIsImlzcyI6ImFnZW50OnlpZGF0YTo6ZjJkYTQ1NmUtYjA5MC00ZjdiLTgyNTYtOWU0ZTFjNTA5ZGRmIiwiaWF0IjoxNTgxNzAzMTU2LCJyb2xlIjpbInVzZXIiLCJ1c2VyX2FwaV9hZG1pbiIsInVzZXJfYXBpX3JlYWQiLCJ1c2VyX2FwaV93cml0ZSJdLCJnZW5lcmFsLXB1cnBvc2UiOmZhbHNlLCJ1cmwiOiIxYTMyMjY4YzA4YjMzYzJiMzlhMjg5MTA4NDE5OGFiZjNjZWExNzdmIn0.QEUxrUZ0uyXu2-cLU6JrhmNHWwScObX0NYghH8UdLP8SJXA6AefVZrtRBINeK6j_iM8ibOzJ19FidH1r5BsJbA\n",
-      "Resolving download.data.world (download.data.world)... 3.222.149.11, 54.86.110.27, 54.85.70.45\n",
-      "Connecting to download.data.world (download.data.world)|3.222.149.11|:443... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: unspecified [application/x-tar]\n",
-      "Saving to: ‘./check_points/512/model_best.pth.tar’\n",
-      "\n",
-      "./check_points/512/     [     <=>            ]  12.08M  13.8MB/s    in 0.9s    \n",
-      "\n",
-      "2020-03-02 19:50:52 (13.8 MB/s) - ‘./check_points/512/model_best.pth.tar’ saved [12662389]\n",
-      "\n"
+      "Dataset is already present. No need to re-download it.\n"
      ]
     }
    ],
@@ -757,7 +726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -766,7 +735,7 @@
        "tensor([[18.7140]], device='cuda:0', grad_fn=<AddmmBackward>)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -785,12 +754,48 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One of the benefits of building a deep learning model is that the [Greeks](<https://en.wikipedia.org/wiki/Greeks_(finance)#First-order_Greeks>) can be easily computed. We just need to take advantage of the auto-grad feature in Pytorch. Following shows an example of calculating the first order differentiation for parameters 'K, B, S0, sigma, mu, r'"
+    "One of the benefits of building a deep learning model is that the [Greeks](<https://en.wikipedia.org/wiki/Greeks_(finance)#First-order_Greeks>) can be easily computed. \n",
+    "We just need to take advantage of the auto-grad feature in Pytorch. Following is an example to compute the first order differentiation for a multiple variable polynomial function. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(tensor([24., 36.], grad_fn=<AddBackward0>),)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from torch.autograd import grad\n",
+    "'''\n",
+    "z = (xy)^2\n",
+    "x = 3, y =2\n",
+    "\n",
+    "first order deriv [24 36]\n",
+    "'''\n",
+    "inputs = torch.tensor([3.0,2.0], requires_grad=True)\n",
+    "z = (inputs[0]*inputs[1])**2\n",
+    "first_order_grad = grad(z, inputs, create_graph=True)\n",
+    "print(first_order_grad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `grad` function to compute the first order differentiation for parameters 'K, B, S0, sigma, mu, r'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -800,7 +805,7 @@
        "         -1.8419e+01]], device='cuda:0')"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -823,16 +828,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fa5e84fa9e8>]"
+       "[<matplotlib.lines.Line2D at 0x7fcd9e1ac7b8>]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -874,22 +879,65 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Calculating the second order derivative is easy in PyTorch too, following is an example:"
+    "Calculating the second order derivative is easy in PyTorch too. We just need to apply the `grad` function twice. Following is an example to calculate the second order derivative for the same polynomial function as above:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([ 8., 24.])\n",
+      "tensor([24., 18.])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from torch.autograd import grad\n",
+    "'''\n",
+    "z = (xy)^2\n",
+    "x = 3, y =2\n",
+    "\n",
+    "first order deriv [24 36]\n",
+    "d2z/dx2 = 8\n",
+    "d2z/dxdy = 24\n",
+    "d2z/dy2 = 18\n",
+    "'''\n",
+    "\n",
+    "inputs = torch.tensor([3.0,2.0], requires_grad=True)\n",
+    "z = (inputs[0]*inputs[1])**2\n",
+    "first_order_grad = grad(z, inputs, create_graph=True)\n",
+    "second_order_grad_x, = grad(first_order_grad[0][0], inputs, retain_graph=True) #\n",
+    "second_order_grad_y, = grad(first_order_grad[0][1], inputs)\n",
+    "print(second_order_grad_x)\n",
+    "print(second_order_grad_y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use this mechanism, we can calculate the second order derivatives $\\frac{\\partial^2 P}{\\partial K \\partial S_0}$, $\\frac{\\partial^2 P}{\\partial B \\partial S_0}$, $\\frac{\\partial^2 P}{\\partial S_0^2}$, $\\frac{\\partial^2 P}{\\partial \\sigma \\partial S_0}$, $\\frac{\\partial^2 P}{\\partial \\mu \\partial S_0}$, $\\frac{\\partial^2 P}{\\partial r \\partial S_0}$ in the following example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(tensor([[ 6.9817e-01, -3.2722e-01,  3.9008e-02, -3.5628e+01, -7.0561e+00,\n",
-       "          -5.2492e+01]], device='cuda:0'),)"
+       "(tensor([[-0.0143,  0.0039,  0.0098, -0.3183,  1.1455, -0.7876]],\n",
+       "        device='cuda:0'),)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -908,7 +956,7 @@
     "# instead of using loss.backward(), use torch.autograd.grad() to compute gradients\n",
     "# https://pytorch.org/docs/stable/autograd.html#torch.autograd.grad\n",
     "loss_grads = grad(x, inputs, create_graph=True)\n",
-    "drv = grad(loss_grads[0], inputs, torch.ones_like(loss_grads[0]) )\n",
+    "drv = grad(loss_grads[0][0][2], inputs)\n",
     "drv"
    ]
   },
@@ -921,22 +969,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fa5e84a69e8>]"
+       "[<matplotlib.lines.Line2D at 0x7f996066dba8>]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -955,7 +1003,7 @@
     "    inputs.requires_grad = True\n",
     "    x = model(inputs)\n",
     "    loss_grads = grad(x, inputs, create_graph=True)\n",
-    "    drv = grad(loss_grads[0], inputs, torch.ones_like(loss_grads[0]) )\n",
+    "    drv = grad(loss_grads[0][0][2], inputs)\n",
     "    return drv[0][0][2]\n",
     "\n",
     "prices = np.arange(10, 200, 0.1)\n",

From 273d50fd4513fc3fcb45f6e35ef7839dbdef8dd7 Mon Sep 17 00:00:00 2001
From: yidong72 <43824965+yidong72@users.noreply.github.com>
Date: Tue, 19 May 2020 15:41:53 -0400
Subject: [PATCH 6/6] [REVIEW] Update to latest version of RAPIDS 0.13 (#81)

* make the code 0.13 compatible
* fixed the unit test bugs
* fixed portfolio notebook
* fixed xgboost node
* get rid of the warning
* fixed the flake8 bugs
* cupy is installed by default
* fixed the remaining notebooks
* remove the default jupyter-lab entrypoint
* activate rapids
* added unit test to cover the return feature
* added 3 node tests
* using the series
* fixed the groupby
* fixed the port bug
---
 docker/build.sh                               |   8 +-
 gquant/cuindicator/ewm.py                     |   6 +-
 gquant/cuindicator/frac_diff.py               |   4 +-
 gquant/cuindicator/indicator.py               | 409 +++++++++---------
 gquant/cuindicator/pewm.py                    |   8 +-
 gquant/cuindicator/rolling.py                 |   4 +-
 gquant/dataframe_flow/_node_flow.py           |  19 +-
 gquant/dataframe_flow/node.py                 |   4 +-
 gquant/plugin_nodes/__init__.py               |  12 +-
 .../plugin_nodes/backtest/simpleBackTest.py   |   1 +
 .../plugin_nodes/dataloader/csvStockLoader.py |  22 +-
 .../strategy/movingAverageStrategyNode.py     |   8 +-
 .../portExpMovingAverageStrategyNode.py       |   8 +-
 .../strategy/xgboostStrategyNode.py           |  24 +-
 .../transform/assetIndicatorNode.py           |   8 +-
 .../plugin_nodes/transform/indicatorNode.py   |   7 +-
 .../transform/returnFeatureNode.py            |  22 +-
 notebooks/01_tutorial.ipynb                   | 333 +++-----------
 notebooks/02_single_stock_trade.ipynb         |  14 +-
 notebooks/03_simple_dask_example.ipynb        | 392 ++---------------
 notebooks/04_portfolio_trade.ipynb            | 146 +++----
 notebooks/05_customize_nodes.ipynb            | 226 +++++-----
 .../05b_customize_nodes_with_ports.ipynb      | 277 ++++++------
 notebooks/06_xgboost_trade.ipynb              | 130 +++---
 notebooks/07_fractional_differencing.ipynb    |  28 +-
 .../asian_barrier_option/mc_pricing.ipynb     |   2 +-
 notebooks/cuIndicator/rsi_perf.ipynb          |  14 +-
 notebooks/custom_port_nodes.py                |  14 +-
 .../mortgage_e2e_gquant.ipynb                 |   2 +-
 tests/unit/test_multi_assets_indicator.py     |   6 +-
 tests/unit/test_nodes.py                      | 174 ++++++++
 tests/unit/test_rolling.py                    |  21 +-
 tests/unit/test_taskgraph_api.py              |   2 +-
 tests/unit/test_util.py                       |   6 +-
 tests/unit/utils.py                           |  23 +
 35 files changed, 1022 insertions(+), 1362 deletions(-)
 create mode 100644 tests/unit/test_nodes.py

diff --git a/docker/build.sh b/docker/build.sh
index 0b1cab85..e9cbb744 100644
--- a/docker/build.sh
+++ b/docker/build.sh
@@ -32,24 +32,21 @@ echo -e "\nPlease, select your cuda version:\n" \
 
 read -p "Enter your option and hit return [1]-3: " CUDA_VERSION
 
-RAPIDS_VERSION="0.11"
+RAPIDS_VERSION="0.13"
 
 CUDA_VERSION=${CUDA_VERSION:-1}
 case $CUDA_VERSION in
     2)
 	echo "cuda 10.0 selected."
 	CONTAINER_VER='10.0'
-	CUPY='cupy-cuda100'
 	;;
     3)
 	echo "cuda 10.1.2 selected."
 	CONTAINER_VER='10.1'
-	CUPY='cupy-cuda101'
 	;;
     *)
 	echo "cuda 9.2 selected."
 	CONTAINER_VER='9.2'
-	CUPY='cupy-cuda92'
 	;;
 esac
 
@@ -82,8 +79,6 @@ SHELL ["bash","-c"]
 #
 # Additional python libs
 #
-RUN source activate rapids \ 
-    && pip install $CUPY
 
 RUN source activate rapids \
     && cd /rapids/gQuant \
@@ -106,6 +101,7 @@ EXPOSE 8888
 EXPOSE 8787
 EXPOSE 8786
 WORKDIR /rapids
+ENTRYPOINT /bin/bash -c 'source activate rapids; /bin/bash'
 EOF
 
 docker build -f $D_FILE -t $D_CONT .
diff --git a/gquant/cuindicator/ewm.py b/gquant/cuindicator/ewm.py
index 29bfe2d4..0c34a76b 100755
--- a/gquant/cuindicator/ewm.py
+++ b/gquant/cuindicator/ewm.py
@@ -49,8 +49,8 @@ def kernel(in_arr, out_arr, average_length, span, arr_len, thread_tile,
         for j in range(0, average_length - 1, block_size):
             if (((tx + j) < average_length - 1) and
                     (starting_id - average_length + 1 + tx + j >= 0)):
-                        shared[tx + j] = \
-                            in_arr[starting_id - average_length + 1 + tx + j]
+                shared[tx + j] = \
+                    in_arr[starting_id - average_length + 1 + tx + j]
         cuda.syncthreads()
         # slice the shared memory for each threads
         start_shared = tx * thread_tile
@@ -95,7 +95,7 @@ def __init__(self, span, input_arr, min_periods=None, thread_tile=48,
         if isinstance(input_arr, numba.cuda.cudadrv.devicearray.DeviceNDArray):
             self.gpu_in = input_arr
         else:
-            self.gpu_in = input_arr.data.to_gpu_array()
+            self.gpu_in = input_arr.to_gpu_array()
         if min_periods is None:
             self.min_periods = span
         else:
diff --git a/gquant/cuindicator/frac_diff.py b/gquant/cuindicator/frac_diff.py
index 68efcde0..ea5d9c67 100644
--- a/gquant/cuindicator/frac_diff.py
+++ b/gquant/cuindicator/frac_diff.py
@@ -189,7 +189,7 @@ def fractional_diff(input_arr, d=0.5, floor=1e-3, min_periods=None,
     if isinstance(input_arr, numba.cuda.cudadrv.devicearray.DeviceNDArray):
         gpu_in = input_arr
     else:
-        gpu_in = input_arr.data.to_gpu_array()
+        gpu_in = input_arr.to_gpu_array()
 
     # compute the weights for the fractional difference
     weights = get_weights_floored(d=d,
@@ -269,6 +269,6 @@ def port_fractional_diff(asset_indicator, input_arr, d=0.5, floor=1e-3,
                                    min_periods=min_periods,
                                    thread_tile=thread_tile,
                                    number_of_threads=number_of_threads)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), out, 0,
+    port_mask_nan(asset_indicator.to_gpu_array(), out, 0,
                   len(weights) - 1)
     return out, weights
diff --git a/gquant/cuindicator/indicator.py b/gquant/cuindicator/indicator.py
index 33a497e9..2fdebc55 100755
--- a/gquant/cuindicator/indicator.py
+++ b/gquant/cuindicator/indicator.py
@@ -24,7 +24,7 @@ def moving_average(close_arr, n):
     :return: moving average in cu.Series
     """
     MA = Rolling(n, close_arr).mean()
-    return cudf.Series(MA)
+    return cudf.Series(MA, nan_as_null=False)
 
 
 def exponential_moving_average(close_arr, n):
@@ -35,7 +35,7 @@ def exponential_moving_average(close_arr, n):
     :return: expoential weighted moving average in cu.Series
     """
     EMA = Ewm(n, close_arr).mean()
-    return cudf.Series(EMA)
+    return cudf.Series(EMA, nan_as_null=False)
 
 
 def port_exponential_moving_average(asset_indicator, close_arr, n):
@@ -48,7 +48,7 @@ def port_exponential_moving_average(asset_indicator, close_arr, n):
     :return: expoential weighted moving average in cu.Series
     """
     EMA = PEwm(n, close_arr, asset_indicator).mean()
-    return cudf.Series(EMA)
+    return cudf.Series(EMA, nan_as_null=False)
 
 
 def port_moving_average(asset_indicator, close_arr, n):
@@ -60,8 +60,8 @@ def port_moving_average(asset_indicator, close_arr, n):
     :return: expoential weighted moving average in cu.Series
     """
     MA = Rolling(n, close_arr).mean()
-    port_mask_nan(asset_indicator.data.to_gpu_array(), MA, 0, n - 1)
-    return cudf.Series(MA)
+    port_mask_nan(asset_indicator.to_gpu_array(), MA, 0, n - 1)
+    return cudf.Series(MA, nan_as_null=False)
 
 
 def momentum(close_arr, n):
@@ -72,7 +72,7 @@ def momentum(close_arr, n):
     :param n: time steps
     :return: momentum in cu.Series
     """
-    return cudf.Series(diff(close_arr, n))
+    return cudf.Series(diff(close_arr, n), nan_as_null=False)
 
 
 def rate_of_change(close_arr, n):
@@ -84,7 +84,7 @@ def rate_of_change(close_arr, n):
     """
     M = diff(close_arr, n - 1)
     N = shift(close_arr, n - 1)
-    return cudf.Series(division(M, N))
+    return cudf.Series(division(M, N), nan_as_null=False)
 
 
 def port_rate_of_change(asset_indicator, close_arr, n):
@@ -99,10 +99,10 @@ def port_rate_of_change(asset_indicator, close_arr, n):
     N = shift(close_arr, n - 1)
     out = division(M, N)
     if n - 1 >= 0:
-        port_mask_nan(asset_indicator.data.to_gpu_array(), out, 0, n - 1)
+        port_mask_nan(asset_indicator.to_gpu_array(), out, 0, n - 1)
     else:
-        port_mask_nan(asset_indicator.data.to_gpu_array(), out, n - 1, 0)
-    return cudf.Series(out)
+        port_mask_nan(asset_indicator.to_gpu_array(), out, n - 1, 0)
+    return cudf.Series(out, nan_as_null=False)
 
 
 def port_diff(asset_indicator, close_arr, n):
@@ -113,12 +113,12 @@ def port_diff(asset_indicator, close_arr, n):
     :param n: time steps
     :return: diff in cu.Series
     """
-    M = diff(close_arr.data.to_gpu_array(), n)
+    M = diff(close_arr.to_gpu_array(), n)
     if n >= 0:
-        port_mask_nan(asset_indicator.data.to_gpu_array(), M, 0, n)
+        port_mask_nan(asset_indicator.to_gpu_array(), M, 0, n)
     else:
-        port_mask_nan(asset_indicator.data.to_gpu_array(), M, n, 0)
-    return cudf.Series(M)
+        port_mask_nan(asset_indicator.to_gpu_array(), M, n, 0)
+    return cudf.Series(M, nan_as_null=False)
 
 
 def port_shift(asset_indicator, close_arr, n):
@@ -129,12 +129,12 @@ def port_shift(asset_indicator, close_arr, n):
     :param n: time steps
     :return: shift in cu.Series
     """
-    M = shift(close_arr.data.to_gpu_array(), n)
+    M = shift(close_arr.to_gpu_array(), n)
     if n >= 0:
-        port_mask_nan(asset_indicator.data.to_gpu_array(), M, 0, n)
+        port_mask_nan(asset_indicator.to_gpu_array(), M, 0, n)
     else:
-        port_mask_nan(asset_indicator.data.to_gpu_array(), M, n, 0)
-    return cudf.Series(M)
+        port_mask_nan(asset_indicator.to_gpu_array(), M, n, 0)
+    return cudf.Series(M, nan_as_null=False)
 
 
 def bollinger_bands(close_arr, n):
@@ -147,15 +147,16 @@ def bollinger_bands(close_arr, n):
     """
     MA = Rolling(n, close_arr).mean()
     MSD = Rolling(n, close_arr).std()
-    close_arr_gpu = numba.cuda.device_array_like(close_arr.data.to_gpu_array())
-    close_arr_gpu[:] = close_arr.data.to_gpu_array()[:]
+    close_arr_gpu = numba.cuda.device_array_like(close_arr.to_gpu_array())
+    close_arr_gpu[:] = close_arr.to_gpu_array()[:]
     close_arr_gpu[0:n-1] = math.nan
     MSD_4 = scale(MSD, 4.0)
     b1 = division(MSD_4, MA)
     b2 = division(summation(substract(close_arr_gpu, MA), scale(MSD, 2.0)),
                   MSD_4)
     out = collections.namedtuple('Bollinger', 'b1 b2')
-    return out(b1=cudf.Series(b1), b2=cudf.Series(b2))
+    return out(b1=cudf.Series(b1, nan_as_null=False),
+               b2=cudf.Series(b2, nan_as_null=False))
 
 
 def port_bollinger_bands(asset_indicator, close_arr, n):
@@ -168,18 +169,19 @@ def port_bollinger_bands(asset_indicator, close_arr, n):
     :return: b1 b2
     """
     MA = Rolling(n, close_arr).mean()
-    port_mask_nan(asset_indicator.data.to_gpu_array(), MA, 0, n - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), MA, 0, n - 1)
     MSD = Rolling(n, close_arr).std()
-    port_mask_nan(asset_indicator.data.to_gpu_array(), MSD, 0, n - 1)
-    close_arr_gpu = numba.cuda.device_array_like(close_arr.data.to_gpu_array())
-    close_arr_gpu[:] = close_arr.data.to_gpu_array()[:]
+    port_mask_nan(asset_indicator.to_gpu_array(), MSD, 0, n - 1)
+    close_arr_gpu = numba.cuda.device_array_like(close_arr.to_gpu_array())
+    close_arr_gpu[:] = close_arr.to_gpu_array()[:]
     close_arr_gpu[0:n-1] = math.nan
     MSD_4 = scale(MSD, 4.0)
     b1 = division(MSD_4, MA)
     b2 = division(summation(substract(close_arr_gpu, MA), scale(MSD, 2.0)),
                   MSD_4)
     out = collections.namedtuple('Bollinger', 'b1 b2')
-    return out(b1=cudf.Series(b1), b2=cudf.Series(b2))
+    return out(b1=cudf.Series(b1, nan_as_null=False),
+               b2=cudf.Series(b2, nan_as_null=False))
 
 
 def trix(close_arr, n):
@@ -192,7 +194,7 @@ def trix(close_arr, n):
     EX1 = Ewm(n, close_arr).mean()
     EX2 = Ewm(n, EX1).mean()
     EX3 = Ewm(n, EX2).mean()
-    return rate_of_change(cudf.Series(EX3), 2)
+    return rate_of_change(cudf.Series(EX3, nan_as_null=False), 2)
 
 
 def port_trix(asset_indicator, close_arr, n):
@@ -206,7 +208,7 @@ def port_trix(asset_indicator, close_arr, n):
     EX1 = PEwm(n, close_arr, asset_indicator).mean()
     EX2 = PEwm(n, EX1, asset_indicator).mean()
     EX3 = PEwm(n, EX2, asset_indicator).mean()
-    return rate_of_change(cudf.Series(EX3), 2)
+    return rate_of_change(cudf.Series(EX3, nan_as_null=False), 2)
 
 
 def macd(close_arr, n_fast, n_slow):
@@ -224,8 +226,9 @@ def macd(close_arr, n_fast, n_slow):
     MACDsign = Ewm(average_window, MACD).mean()
     MACDdiff = substract(MACD, MACDsign)
     out = collections.namedtuple('MACD', 'MACD MACDsign MACDdiff')
-    return out(MACD=cudf.Series(MACD), MACDsign=cudf.Series(MACDsign),
-               MACDdiff=cudf.Series(MACDdiff))
+    return out(MACD=cudf.Series(MACD, nan_as_null=False),
+               MACDsign=cudf.Series(MACDsign, nan_as_null=False),
+               MACDdiff=cudf.Series(MACDdiff, nan_as_null=False))
 
 
 def port_macd(asset_indicator, close_arr, n_fast, n_slow):
@@ -244,8 +247,9 @@ def port_macd(asset_indicator, close_arr, n_fast, n_slow):
     MACDsign = PEwm(average_window, MACD, asset_indicator).mean()
     MACDdiff = substract(MACD, MACDsign)
     out = collections.namedtuple('MACD', 'MACD MACDsign MACDdiff')
-    return out(MACD=cudf.Series(MACD), MACDsign=cudf.Series(MACDsign),
-               MACDdiff=cudf.Series(MACDdiff))
+    return out(MACD=cudf.Series(MACD, nan_as_null=False),
+               MACDsign=cudf.Series(MACDsign, nan_as_null=False),
+               MACDdiff=cudf.Series(MACDdiff, nan_as_null=False))
 
 
 def average_true_range(high_arr, low_arr, close_arr, n):
@@ -258,10 +262,10 @@ def average_true_range(high_arr, low_arr, close_arr, n):
     :param n: time steps
     :return: average true range indicator
     """
-    tr = true_range(high_arr.data.to_gpu_array(), low_arr.data.to_gpu_array(),
-                    close_arr.data.to_gpu_array())
+    tr = true_range(high_arr.to_gpu_array(), low_arr.to_gpu_array(),
+                    close_arr.to_gpu_array())
     ATR = Ewm(n, tr).mean()
-    return cudf.Series(ATR)
+    return cudf.Series(ATR, nan_as_null=False)
 
 
 def port_average_true_range(asset_indicator, high_arr,
@@ -275,12 +279,12 @@ def port_average_true_range(asset_indicator, high_arr,
     :param n: time steps
     :return: average true range indicator
     """
-    tr = port_true_range(asset_indicator.data.to_gpu_array(),
-                         high_arr.data.to_gpu_array(),
-                         low_arr.data.to_gpu_array(),
-                         close_arr.data.to_gpu_array())
+    tr = port_true_range(asset_indicator.to_gpu_array(),
+                         high_arr.to_gpu_array(),
+                         low_arr.to_gpu_array(),
+                         close_arr.to_gpu_array())
     ATR = PEwm(n, tr, asset_indicator).mean()
-    return cudf.Series(ATR)
+    return cudf.Series(ATR, nan_as_null=False)
 
 
 def ppsr(high_arr, low_arr, close_arr):
@@ -291,9 +295,9 @@ def ppsr(high_arr, low_arr, close_arr):
     :param close_arr: close price of the bar, expect series from cudf
     :return: PP R1 S1 R2 S2 R3 S3
     """
-    high_gpu = high_arr.data.to_gpu_array()
-    low_gpu = low_arr.data.to_gpu_array()
-    close_gpu = close_arr.data.to_gpu_array()
+    high_gpu = high_arr.to_gpu_array()
+    low_gpu = low_arr.to_gpu_array()
+    close_gpu = close_arr.to_gpu_array()
     PP = average_price(high_gpu, low_gpu, close_gpu)
     R1 = substract(scale(PP, 2.0), low_gpu)
     S1 = substract(scale(PP, 2.0), high_gpu)
@@ -302,13 +306,13 @@ def ppsr(high_arr, low_arr, close_arr):
     R3 = summation(high_gpu, scale(substract(PP, low_gpu), 2.0))
     S3 = substract(low_gpu, scale(substract(high_gpu, PP), 2.0))
     out = collections.namedtuple('PPSR', 'PP R1 S1 R2 S2 R3 S3')
-    return out(PP=cudf.Series(PP),
-               R1=cudf.Series(R1),
-               S1=cudf.Series(S1),
-               R2=cudf.Series(R2),
-               S2=cudf.Series(S2),
-               R3=cudf.Series(R3),
-               S3=cudf.Series(S3))
+    return out(PP=cudf.Series(PP, nan_as_null=False),
+               R1=cudf.Series(R1, nan_as_null=False),
+               S1=cudf.Series(S1, nan_as_null=False),
+               R2=cudf.Series(R2, nan_as_null=False),
+               S2=cudf.Series(S2, nan_as_null=False),
+               R3=cudf.Series(R3, nan_as_null=False),
+               S3=cudf.Series(S3, nan_as_null=False))
 
 
 def port_ppsr(asset_indicator, high_arr, low_arr, close_arr):
@@ -320,9 +324,9 @@ def port_ppsr(asset_indicator, high_arr, low_arr, close_arr):
     :param close_arr: close price of the bar, expect series from cudf
     :return: PP R1 S1 R2 S2 R3 S3
     """
-    high_gpu = high_arr.data.to_gpu_array()
-    low_gpu = low_arr.data.to_gpu_array()
-    close_gpu = close_arr.data.to_gpu_array()
+    high_gpu = high_arr.to_gpu_array()
+    low_gpu = low_arr.to_gpu_array()
+    close_gpu = close_arr.to_gpu_array()
     PP = average_price(high_gpu, low_gpu, close_gpu)
     R1 = substract(scale(PP, 2.0), low_gpu)
     S1 = substract(scale(PP, 2.0), high_gpu)
@@ -331,13 +335,13 @@ def port_ppsr(asset_indicator, high_arr, low_arr, close_arr):
     R3 = summation(high_gpu, scale(substract(PP, low_gpu), 2.0))
     S3 = substract(low_gpu, scale(substract(high_gpu, PP), 2.0))
     out = collections.namedtuple('PPSR', 'PP R1 S1 R2 S2 R3 S3')
-    return out(PP=cudf.Series(PP),
-               R1=cudf.Series(R1),
-               S1=cudf.Series(S1),
-               R2=cudf.Series(R2),
-               S2=cudf.Series(S2),
-               R3=cudf.Series(R3),
-               S3=cudf.Series(S3))
+    return out(PP=cudf.Series(PP, nan_as_null=False),
+               R1=cudf.Series(R1, nan_as_null=False),
+               S1=cudf.Series(S1, nan_as_null=False),
+               R2=cudf.Series(R2, nan_as_null=False),
+               S2=cudf.Series(S2, nan_as_null=False),
+               R3=cudf.Series(R3, nan_as_null=False),
+               S3=cudf.Series(S3, nan_as_null=False))
 
 
 def stochastic_oscillator_k(high_arr, low_arr, close_arr):
@@ -377,7 +381,7 @@ def stochastic_oscillator_d(high_arr, low_arr, close_arr, n):
     """
     SOk = stochastic_oscillator_k(high_arr, low_arr, close_arr)
     SOd = Ewm(n, SOk).mean()
-    return cudf.Series(SOd)
+    return cudf.Series(SOd, nan_as_null=False)
 
 
 def port_stochastic_oscillator_d(asset_indicator, high_arr, low_arr,
@@ -393,7 +397,7 @@ def port_stochastic_oscillator_d(asset_indicator, high_arr, low_arr,
     """
     SOk = stochastic_oscillator_k(high_arr, low_arr, close_arr)
     SOd = PEwm(n, SOk, asset_indicator).mean()
-    return cudf.Series(SOd)
+    return cudf.Series(SOd, nan_as_null=False)
 
 
 def average_directional_movement_index(high_arr, low_arr, close_arr, n, n_ADX):
@@ -406,17 +410,17 @@ def average_directional_movement_index(high_arr, low_arr, close_arr, n, n_ADX):
     :param n_ADX: time steps to do EWM average of ADX
     :return: Average Directional Movement Index in cudf.Series
     """
-    UpI, DoI = upDownMove(high_arr.data.to_gpu_array(),
-                          low_arr.data.to_gpu_array())
+    UpI, DoI = upDownMove(high_arr.to_gpu_array(),
+                          low_arr.to_gpu_array())
     last_ele = len(high_arr) - 1
-    tr = true_range(high_arr.data.to_gpu_array(), low_arr.data.to_gpu_array(),
-                    close_arr.data.to_gpu_array())
+    tr = true_range(high_arr.to_gpu_array(), low_arr.to_gpu_array(),
+                    close_arr.to_gpu_array())
     ATR = Ewm(n, tr).mean()
     PosDI = division(Ewm(n, UpI).mean(), ATR)
     NegDI = division(Ewm(n, DoI).mean(), ATR)
     NORM = division(abs_arr(substract(PosDI, NegDI)), summation(PosDI, NegDI))
     NORM[last_ele] = math.nan
-    ADX = cudf.Series(Ewm(n_ADX, NORM).mean())
+    ADX = cudf.Series(Ewm(n_ADX, NORM).mean(), nan_as_null=False)
     return ADX
 
 
@@ -433,18 +437,19 @@ def port_average_directional_movement_index(asset_indicator,
     :param n_ADX: time steps to do EWM average of ADX
     :return: Average Directional Movement Index in cudf.Series
     """
-    UpI, DoI = upDownMove(high_arr.data.to_gpu_array(),
-                          low_arr.data.to_gpu_array())
+    UpI, DoI = upDownMove(high_arr.to_gpu_array(),
+                          low_arr.to_gpu_array())
     tr = port_true_range(asset_indicator.to_gpu_array(),
-                         high_arr.data.to_gpu_array(),
-                         low_arr.data.to_gpu_array(),
-                         close_arr.data.to_gpu_array())
+                         high_arr.to_gpu_array(),
+                         low_arr.to_gpu_array(),
+                         close_arr.to_gpu_array())
     ATR = PEwm(n, tr, asset_indicator).mean()
     PosDI = division(PEwm(n, UpI, asset_indicator).mean(), ATR)
     NegDI = division(PEwm(n, DoI, asset_indicator).mean(), ATR)
     NORM = division(abs_arr(substract(PosDI, NegDI)), summation(PosDI, NegDI))
-    port_mask_nan(asset_indicator.data.to_gpu_array(), NORM, -1, 0)
-    ADX = cudf.Series(PEwm(n_ADX, NORM, asset_indicator).mean())
+    port_mask_nan(asset_indicator.to_gpu_array(), NORM, -1, 0)
+    ADX = cudf.Series(PEwm(n_ADX, NORM, asset_indicator).mean(),
+                      nan_as_null=False)
     return ADX
 
 
@@ -460,14 +465,14 @@ def vortex_indicator(high_arr, low_arr, close_arr, n):
     :param n: time steps to do EWM average
     :return:  Vortex Indicator in cudf.Series
     """
-    TR = true_range(high_arr.data.to_gpu_array(), low_arr.data.to_gpu_array(),
-                    close_arr.data.to_gpu_array())
+    TR = true_range(high_arr.to_gpu_array(), low_arr.to_gpu_array(),
+                    close_arr.to_gpu_array())
 
-    VM = lowhigh_diff(high_arr.data.to_gpu_array(),
-                      low_arr.data.to_gpu_array())
+    VM = lowhigh_diff(high_arr.to_gpu_array(),
+                      low_arr.to_gpu_array())
 
     VI = division(Rolling(n, VM).sum(), Rolling(n, TR).sum())
-    return cudf.Series(VI)
+    return cudf.Series(VI, nan_as_null=False)
 
 
 def port_vortex_indicator(asset_indicator, high_arr, low_arr, close_arr, n):
@@ -484,17 +489,17 @@ def port_vortex_indicator(asset_indicator, high_arr, low_arr, close_arr, n):
     :return:  Vortex Indicator in cudf.Series
     """
     TR = port_true_range(asset_indicator.to_gpu_array(),
-                         high_arr.data.to_gpu_array(),
-                         low_arr.data.to_gpu_array(),
-                         close_arr.data.to_gpu_array())
+                         high_arr.to_gpu_array(),
+                         low_arr.to_gpu_array(),
+                         close_arr.to_gpu_array())
 
     VM = port_lowhigh_diff(asset_indicator.to_gpu_array(),
-                           high_arr.data.to_gpu_array(),
-                           low_arr.data.to_gpu_array())
+                           high_arr.to_gpu_array(),
+                           low_arr.to_gpu_array())
 
     VI = division(Rolling(n, VM).sum(), Rolling(n, TR).sum())
-    port_mask_nan(asset_indicator.data.to_gpu_array(), VI, 0, n - 1)
-    return cudf.Series(VI)
+    port_mask_nan(asset_indicator.to_gpu_array(), VI, 0, n - 1)
+    return cudf.Series(VI, nan_as_null=False)
 
 
 def kst_oscillator(close_arr, r1, r2, r3, r4, n1, n2, n3, n4):
@@ -524,7 +529,7 @@ def kst_oscillator(close_arr, r1, r2, r3, r4, n1, n2, n3, n4):
     term3 = scale(Rolling(n3, division(M3, N3)).sum(), 3.0)
     term4 = scale(Rolling(n4, division(M4, N4)).sum(), 4.0)
     KST = summation(summation(summation(term1, term2), term3), term4)
-    return cudf.Series(KST)
+    return cudf.Series(KST, nan_as_null=False)
 
 
 def port_kst_oscillator(asset_indicator, close_arr,
@@ -545,30 +550,30 @@ def port_kst_oscillator(asset_indicator, close_arr,
     """
     M1 = diff(close_arr, r1 - 1)
     N1 = shift(close_arr, r1 - 1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), M1, 0, r1 - 1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), N1, 0, r1 - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), M1, 0, r1 - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), N1, 0, r1 - 1)
     M2 = diff(close_arr, r2 - 1)
     N2 = shift(close_arr, r2 - 1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), M2, 0, r2 - 1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), N2, 0, r2 - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), M2, 0, r2 - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), N2, 0, r2 - 1)
     M3 = diff(close_arr, r3 - 1)
     N3 = shift(close_arr, r3 - 1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), M3, 0, r3 - 1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), N3, 0, r3 - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), M3, 0, r3 - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), N3, 0, r3 - 1)
     M4 = diff(close_arr, r4 - 1)
     N4 = shift(close_arr, r4 - 1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), M4, 0, r4 - 1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), N4, 0, r4 - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), M4, 0, r4 - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), N4, 0, r4 - 1)
     term1 = Rolling(n1, division(M1, N1)).sum()
-    port_mask_nan(asset_indicator.data.to_gpu_array(), term1, 0, n1 - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), term1, 0, n1 - 1)
     term2 = scale(Rolling(n2, division(M2, N2)).sum(), 2.0)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), term2, 0, n2 - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), term2, 0, n2 - 1)
     term3 = scale(Rolling(n3, division(M3, N3)).sum(), 3.0)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), term3, 0, n3 - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), term3, 0, n3 - 1)
     term4 = scale(Rolling(n4, division(M4, N4)).sum(), 4.0)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), term4, 0, n4 - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), term4, 0, n4 - 1)
     KST = summation(summation(summation(term1, term2), term3), term4)
-    return cudf.Series(KST)
+    return cudf.Series(KST, nan_as_null=False)
 
 
 def relative_strength_index(high_arr, low_arr, n):
@@ -579,8 +584,8 @@ def relative_strength_index(high_arr, low_arr, n):
     :param n: time steps to do EWM average
     :return: Relative Strength Index in cudf.Series
     """
-    UpI, DoI = upDownMove(high_arr.data.to_gpu_array(),
-                          low_arr.data.to_gpu_array())
+    UpI, DoI = upDownMove(high_arr.to_gpu_array(),
+                          low_arr.to_gpu_array())
     UpI_s = shift(UpI, 1)
     UpI_s[0] = 0
     DoI_s = shift(DoI, 1)
@@ -588,7 +593,7 @@ def relative_strength_index(high_arr, low_arr, n):
     PosDI = Ewm(n, UpI_s).mean()
     NegDI = Ewm(n, DoI_s).mean()
     RSI = division(PosDI, summation(PosDI, NegDI))
-    return cudf.Series(RSI)
+    return cudf.Series(RSI, nan_as_null=False)
 
 
 def port_relative_strength_index(asset_indicator, high_arr, low_arr, n):
@@ -600,18 +605,24 @@ def port_relative_strength_index(asset_indicator, high_arr, low_arr, n):
     :param n: time steps to do EWM average
     :return: Relative Strength Index in cudf.Series
     """
-    UpI, DoI = upDownMove(high_arr.data.to_gpu_array(),
-                          low_arr.data.to_gpu_array())
+    UpI, DoI = upDownMove(high_arr.to_gpu_array(),
+                          low_arr.to_gpu_array())
     UpI_s = shift(UpI, 1)
     UpI_s[0] = 0
-    UpI_s = cudf.Series(UpI_s) * (1.0 - asset_indicator.reset_index(drop=True))
+    UpI_s = cudf.Series(UpI_s,
+                        nan_as_null=False) * (1.0
+                                              - asset_indicator.reset_index(
+                                                  drop=True))
     DoI_s = shift(DoI, 1)
     DoI_s[0] = 0
-    DoI_s = cudf.Series(DoI_s) * (1.0 - asset_indicator.reset_index(drop=True))
+    DoI_s = cudf.Series(DoI_s,
+                        nan_as_null=False) * (1.0
+                                              - asset_indicator.reset_index(
+                                                  drop=True))
     PosDI = PEwm(n, UpI_s, asset_indicator).mean()
     NegDI = PEwm(n, DoI_s, asset_indicator).mean()
     RSI = division(PosDI, summation(PosDI, NegDI))
-    return cudf.Series(RSI)
+    return cudf.Series(RSI, nan_as_null=False)
 
 
 def mass_index(high_arr, low_arr, n1, n2):
@@ -628,7 +639,7 @@ def mass_index(high_arr, low_arr, n1, n2):
     EX2 = Ewm(n1, EX1).mean()
     Mass = division(EX1, EX2)
     MassI = Rolling(n2, Mass).sum()
-    return cudf.Series(MassI)
+    return cudf.Series(MassI, nan_as_null=False)
 
 
 def port_mass_index(asset_indicator, high_arr, low_arr, n1, n2):
@@ -646,8 +657,8 @@ def port_mass_index(asset_indicator, high_arr, low_arr, n1, n2):
     EX2 = PEwm(n1, EX1, asset_indicator).mean()
     Mass = division(EX1, EX2)
     MassI = Rolling(n2, Mass).sum()
-    port_mask_nan(asset_indicator.data.to_gpu_array(), MassI, 0, n2 - 1)
-    return cudf.Series(MassI)
+    port_mask_nan(asset_indicator.to_gpu_array(), MassI, 0, n2 - 1)
+    return cudf.Series(MassI, nan_as_null=False)
 
 
 def true_strength_index(close_arr, r, s):
@@ -665,7 +676,7 @@ def true_strength_index(close_arr, r, s):
     EMA2 = Ewm(s, EMA1).mean()
     aEMA2 = Ewm(s, aEMA1).mean()
     TSI = division(EMA2, aEMA2)
-    return cudf.Series(TSI)
+    return cudf.Series(TSI, nan_as_null=False)
 
 
 def port_true_strength_index(asset_indicator, close_arr, r, s):
@@ -678,14 +689,14 @@ def port_true_strength_index(asset_indicator, close_arr, r, s):
     :return: True Strength Index in cudf.Series
     """
     M = diff(close_arr, 1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), M, 0, 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), M, 0, 1)
     aM = abs_arr(M)
     EMA1 = PEwm(r, M, asset_indicator).mean()
     aEMA1 = PEwm(r, aM, asset_indicator).mean()
     EMA2 = PEwm(s, EMA1, asset_indicator).mean()
     aEMA2 = PEwm(s, aEMA1, asset_indicator).mean()
     TSI = division(EMA2, aEMA2)
-    return cudf.Series(TSI)
+    return cudf.Series(TSI, nan_as_null=False)
 
 
 def chaikin_oscillator(high_arr, low_arr, close_arr, volume_arr, n1, n2):
@@ -701,7 +712,9 @@ def chaikin_oscillator(high_arr, low_arr, close_arr, volume_arr, n1, n2):
     """
     ad = (2.0 * close_arr - high_arr - low_arr) / (
         high_arr - low_arr) * volume_arr
-    Chaikin = cudf.Series(Ewm(n1, ad).mean()) - cudf.Series(Ewm(n2, ad).mean())
+    Chaikin = cudf.Series(Ewm(n1, ad).mean(),
+                          nan_as_null=False) - cudf.Series(Ewm(n2, ad).mean(),
+                                                           nan_as_null=False)
     return Chaikin
 
 
@@ -722,7 +735,7 @@ def port_chaikin_oscillator(asset_indicator, high_arr, low_arr,
         high_arr - low_arr) * volume_arr
     first = PEwm(n1, ad, asset_indicator).mean()
     second = PEwm(n2, ad, asset_indicator).mean()
-    Chaikin = cudf.Series(substract(first, second))
+    Chaikin = cudf.Series(substract(first, second), nan_as_null=False)
     return Chaikin
 
 
@@ -736,15 +749,15 @@ def money_flow_index(high_arr, low_arr, close_arr, volume_arr, n):
     :param n: time steps
     :return: Money Flow Index in cudf.Series
     """
-    PP = average_price(high_arr.data.to_gpu_array(),
-                       low_arr.data.to_gpu_array(),
-                       close_arr.data.to_gpu_array())
+    PP = average_price(high_arr.to_gpu_array(),
+                       low_arr.to_gpu_array(),
+                       close_arr.to_gpu_array())
 
-    PosMF = money_flow(PP, volume_arr.data.to_gpu_array())
+    PosMF = money_flow(PP, volume_arr.to_gpu_array())
     MFR = division(PosMF,
-                   (multiply(PP, volume_arr.data.to_gpu_array())))  # TotMF
+                   (multiply(PP, volume_arr.to_gpu_array())))  # TotMF
     MFI = Rolling(n, MFR).mean()
-    return cudf.Series(MFI)
+    return cudf.Series(MFI, nan_as_null=False)
 
 
 def port_money_flow_index(asset_indicator, high_arr, low_arr,
@@ -759,17 +772,17 @@ def port_money_flow_index(asset_indicator, high_arr, low_arr,
     :param n: time steps
     :return: Money Flow Index in cudf.Series
     """
-    PP = average_price(high_arr.data.to_gpu_array(),
-                       low_arr.data.to_gpu_array(),
-                       close_arr.data.to_gpu_array())
+    PP = average_price(high_arr.to_gpu_array(),
+                       low_arr.to_gpu_array(),
+                       close_arr.to_gpu_array())
 
-    PosMF = port_money_flow(asset_indicator.data.to_gpu_array(), PP,
-                            volume_arr.data.to_gpu_array())
+    PosMF = port_money_flow(asset_indicator.to_gpu_array(), PP,
+                            volume_arr.to_gpu_array())
     MFR = division(PosMF,
-                   (multiply(PP, volume_arr.data.to_gpu_array())))  # TotMF
+                   (multiply(PP, volume_arr.to_gpu_array())))  # TotMF
     MFI = Rolling(n, MFR).mean()
-    port_mask_nan(asset_indicator.data.to_gpu_array(), MFI, 0, n - 1)
-    return cudf.Series(MFI)
+    port_mask_nan(asset_indicator.to_gpu_array(), MFI, 0, n - 1)
+    return cudf.Series(MFI, nan_as_null=False)
 
 
 def on_balance_volume(close_arr, volume_arr, n):
@@ -780,10 +793,10 @@ def on_balance_volume(close_arr, volume_arr, n):
     :param n: time steps
     :return: On-Balance Volume in cudf.Series
     """
-    OBV = onbalance_volume(close_arr.data.to_gpu_array(),
-                           volume_arr.data.to_gpu_array())
+    OBV = onbalance_volume(close_arr.to_gpu_array(),
+                           volume_arr.to_gpu_array())
     OBV_ma = Rolling(n, OBV).mean()
-    return cudf.Series(OBV_ma)
+    return cudf.Series(OBV_ma, nan_as_null=False)
 
 
 def port_on_balance_volume(asset_indicator, close_arr, volume_arr, n):
@@ -795,12 +808,12 @@ def port_on_balance_volume(asset_indicator, close_arr, volume_arr, n):
     :param n: time steps
     :return: On-Balance Volume in cudf.Series
     """
-    OBV = port_onbalance_volume(asset_indicator.data.to_gpu_array(),
-                                close_arr.data.to_gpu_array(),
-                                volume_arr.data.to_gpu_array())
+    OBV = port_onbalance_volume(asset_indicator.to_gpu_array(),
+                                close_arr.to_gpu_array(),
+                                volume_arr.to_gpu_array())
     OBV_ma = Rolling(n, OBV).mean()
-    port_mask_nan(asset_indicator.data.to_gpu_array(), OBV_ma, 0, n - 1)
-    return cudf.Series(OBV_ma)
+    port_mask_nan(asset_indicator.to_gpu_array(), OBV_ma, 0, n - 1)
+    return cudf.Series(OBV_ma, nan_as_null=False)
 
 
 def force_index(close_arr, volume_arr, n):
@@ -812,7 +825,7 @@ def force_index(close_arr, volume_arr, n):
     :return: Force Index in cudf.Series
     """
     F = multiply(diff(close_arr, n), diff(volume_arr, n))
-    return cudf.Series(F)
+    return cudf.Series(F, nan_as_null=False)
 
 
 def port_force_index(asset_indicator, close_arr, volume_arr, n):
@@ -825,8 +838,8 @@ def port_force_index(asset_indicator, close_arr, volume_arr, n):
     :return: Force Index in cudf.Series
     """
     F = multiply(diff(close_arr, n), diff(volume_arr, n))
-    port_mask_nan(asset_indicator.data.to_gpu_array(), F, 0, n)
-    return cudf.Series(F)
+    port_mask_nan(asset_indicator.to_gpu_array(), F, 0, n)
+    return cudf.Series(F, nan_as_null=False)
 
 
 def ease_of_movement(high_arr, low_arr, volume_arr, n):
@@ -838,15 +851,15 @@ def ease_of_movement(high_arr, low_arr, volume_arr, n):
     :param n: time steps
     :return: Ease of Movement in cudf.Series
     """
-    high_arr_gpu = high_arr.data.to_gpu_array()
-    low_arr_gpu = low_arr.data.to_gpu_array()
+    high_arr_gpu = high_arr.to_gpu_array()
+    low_arr_gpu = low_arr.to_gpu_array()
 
     EoM = division(multiply(summation(diff(high_arr_gpu, 1),
                                       diff(low_arr_gpu, 1)),
                             substract(high_arr_gpu, low_arr_gpu)),
-                   scale(volume_arr.data.to_gpu_array(), 2.0))
+                   scale(volume_arr.to_gpu_array(), 2.0))
     Eom_ma = Rolling(n, EoM).mean()
-    return cudf.Series(Eom_ma)
+    return cudf.Series(Eom_ma, nan_as_null=False)
 
 
 def port_ease_of_movement(asset_indicator, high_arr, low_arr, volume_arr, n):
@@ -859,17 +872,17 @@ def port_ease_of_movement(asset_indicator, high_arr, low_arr, volume_arr, n):
     :param n: time steps
     :return: Ease of Movement in cudf.Series
     """
-    high_arr_gpu = high_arr.data.to_gpu_array()
-    low_arr_gpu = low_arr.data.to_gpu_array()
+    high_arr_gpu = high_arr.to_gpu_array()
+    low_arr_gpu = low_arr.to_gpu_array()
 
     EoM = division(multiply(summation(diff(high_arr_gpu, 1),
                                       diff(low_arr_gpu, 1)),
                             substract(high_arr_gpu, low_arr_gpu)),
-                   scale(volume_arr.data.to_gpu_array(), 2.0))
-    port_mask_nan(asset_indicator.data.to_gpu_array(), EoM, 0, 1)
+                   scale(volume_arr.to_gpu_array(), 2.0))
+    port_mask_nan(asset_indicator.to_gpu_array(), EoM, 0, 1)
     Eom_ma = Rolling(n, EoM).mean()
-    port_mask_nan(asset_indicator.data.to_gpu_array(), Eom_ma, 0, n - 1)
-    return cudf.Series(Eom_ma)
+    port_mask_nan(asset_indicator.to_gpu_array(), Eom_ma, 0, n - 1)
+    return cudf.Series(Eom_ma, nan_as_null=False)
 
 
 def ultimate_oscillator(high_arr, low_arr, close_arr):
@@ -880,16 +893,16 @@ def ultimate_oscillator(high_arr, low_arr, close_arr):
     :param close_arr: close price of the bar, expect series from cudf
     :return: Ultimate Oscillator in cudf.Series
     """
-    TR_l, BP_l = ultimate_osc(high_arr.data.to_gpu_array(),
-                              low_arr.data.to_gpu_array(),
-                              close_arr.data.to_gpu_array())
+    TR_l, BP_l = ultimate_osc(high_arr.to_gpu_array(),
+                              low_arr.to_gpu_array(),
+                              close_arr.to_gpu_array())
     term1 = division(scale(Rolling(7, BP_l).sum(), 4.0),
                      Rolling(7, TR_l).sum())
     term2 = division(scale(Rolling(14, BP_l).sum(), 2.0),
                      Rolling(14, TR_l).sum())
     term3 = division(Rolling(28, BP_l).sum(), Rolling(28, TR_l).sum())
     UltO = summation(summation(term1, term2), term3)
-    return cudf.Series(UltO)
+    return cudf.Series(UltO, nan_as_null=False)
 
 
 def port_ultimate_oscillator(asset_indicator, high_arr, low_arr, close_arr):
@@ -901,20 +914,20 @@ def port_ultimate_oscillator(asset_indicator, high_arr, low_arr, close_arr):
     :param close_arr: close price of the bar, expect series from cudf
     :return: Ultimate Oscillator in cudf.Series
     """
-    TR_l, BP_l = port_ultimate_osc(asset_indicator.data.to_gpu_array(),
-                                   high_arr.data.to_gpu_array(),
-                                   low_arr.data.to_gpu_array(),
-                                   close_arr.data.to_gpu_array())
+    TR_l, BP_l = port_ultimate_osc(asset_indicator.to_gpu_array(),
+                                   high_arr.to_gpu_array(),
+                                   low_arr.to_gpu_array(),
+                                   close_arr.to_gpu_array())
     term1 = division(scale(Rolling(7, BP_l).sum(), 4.0),
                      Rolling(7, TR_l).sum())
     term2 = division(scale(Rolling(14, BP_l).sum(), 2.0),
                      Rolling(14, TR_l).sum())
     term3 = division(Rolling(28, BP_l).sum(), Rolling(28, TR_l).sum())
-    port_mask_nan(asset_indicator.data.to_gpu_array(), term1, 0, 6)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), term2, 0, 13)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), term3, 0, 27)
+    port_mask_nan(asset_indicator.to_gpu_array(), term1, 0, 6)
+    port_mask_nan(asset_indicator.to_gpu_array(), term2, 0, 13)
+    port_mask_nan(asset_indicator.to_gpu_array(), term3, 0, 27)
     UltO = summation(summation(term1, term2), term3)
-    return cudf.Series(UltO)
+    return cudf.Series(UltO, nan_as_null=False)
 
 
 def donchian_channel(high_arr, low_arr, n):
@@ -930,7 +943,7 @@ def donchian_channel(high_arr, low_arr, n):
     dc_l = substract(max_high, min_low)
     dc_l[:n-1] = 0.0
     donchian_chan = shift(dc_l, n - 1)
-    return cudf.Series(donchian_chan)
+    return cudf.Series(donchian_chan, nan_as_null=False)
 
 
 def port_donchian_channel(asset_indicator, high_arr, low_arr, n):
@@ -943,15 +956,15 @@ def port_donchian_channel(asset_indicator, high_arr, low_arr, n):
     :return: donchian channel in cudf.Series
     """
     max_high = Rolling(n, high_arr).max()
-    port_mask_nan(asset_indicator.data.to_gpu_array(), max_high, 0, n - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), max_high, 0, n - 1)
     min_low = Rolling(n, low_arr).min()
-    port_mask_nan(asset_indicator.data.to_gpu_array(), min_low, 0, n - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), min_low, 0, n - 1)
     dc_l = substract(max_high, min_low)
     # dc_l[:n-1] = 0.0
-    port_mask_zero(asset_indicator.data.to_gpu_array(), dc_l, 0, n - 1)
+    port_mask_zero(asset_indicator.to_gpu_array(), dc_l, 0, n - 1)
     donchian_chan = shift(dc_l, n - 1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), donchian_chan, 0, n - 1)
-    return cudf.Series(donchian_chan)
+    port_mask_nan(asset_indicator.to_gpu_array(), donchian_chan, 0, n - 1)
+    return cudf.Series(donchian_chan, nan_as_null=False)
 
 
 def keltner_channel(high_arr, low_arr, close_arr, n):
@@ -964,11 +977,11 @@ def keltner_channel(high_arr, low_arr, close_arr, n):
     :return: Keltner Channel in cudf.Series
     """
     M = ((high_arr + low_arr + close_arr) / 3.0)
-    KelChM = cudf.Series(Rolling(n, M).mean())
+    KelChM = cudf.Series(Rolling(n, M).mean(), nan_as_null=False)
     U = ((4.0 * high_arr - 2.0 * low_arr + close_arr) / 3.0)
-    KelChU = cudf.Series(Rolling(n, U).mean())
+    KelChU = cudf.Series(Rolling(n, U).mean(), nan_as_null=False)
     D = ((-2.0 * high_arr + 4.0 * low_arr + close_arr) / 3.0)
-    KelChD = cudf.Series(Rolling(n, D).mean())
+    KelChD = cudf.Series(Rolling(n, D).mean(), nan_as_null=False)
     out = collections.namedtuple('Keltner', 'KelChM KelChU KelChD')
     return out(KelChM=KelChM, KelChU=KelChU, KelChD=KelChD)
 
@@ -985,17 +998,17 @@ def port_keltner_channel(asset_indicator, high_arr, low_arr, close_arr, n):
     """
     M = ((high_arr + low_arr + close_arr) / 3.0)
     KelChM = Rolling(n, M).mean()
-    port_mask_nan(asset_indicator.data.to_gpu_array(), KelChM, 0, n - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), KelChM, 0, n - 1)
     U = ((4.0 * high_arr - 2.0 * low_arr + close_arr) / 3.0)
     KelChU = Rolling(n, U).mean()
-    port_mask_nan(asset_indicator.data.to_gpu_array(), KelChU, 0, n - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), KelChU, 0, n - 1)
     D = ((-2.0 * high_arr + 4.0 * low_arr + close_arr) / 3.0)
     KelChD = Rolling(n, D).mean()
-    port_mask_nan(asset_indicator.data.to_gpu_array(), KelChD, 0, n - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), KelChD, 0, n - 1)
     out = collections.namedtuple('Keltner', 'KelChM KelChU KelChD')
-    return out(KelChM=cudf.Series(KelChM),
-               KelChU=cudf.Series(KelChU),
-               KelChD=cudf.Series(KelChD))
+    return out(KelChM=cudf.Series(KelChM, nan_as_null=False),
+               KelChU=cudf.Series(KelChU, nan_as_null=False),
+               KelChD=cudf.Series(KelChD, nan_as_null=False))
 
 
 def coppock_curve(close_arr, n):
@@ -1012,7 +1025,7 @@ def coppock_curve(close_arr, n):
     N = shift(close_arr, int(n * 14 / 10) - 1)
     ROC2 = division(M, N)
     Copp = Ewm(n, summation(ROC1, ROC2)).mean()
-    return cudf.Series(Copp)
+    return cudf.Series(Copp, nan_as_null=False)
 
 
 def port_coppock_curve(asset_indicator, close_arr, n):
@@ -1025,20 +1038,20 @@ def port_coppock_curve(asset_indicator, close_arr, n):
     """
     M = diff(close_arr, int(n * 11 / 10) - 1)
     N = shift(close_arr, int(n * 11 / 10) - 1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), M, 0,
+    port_mask_nan(asset_indicator.to_gpu_array(), M, 0,
                   int(n * 11 / 10) - 1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), N, 0,
+    port_mask_nan(asset_indicator.to_gpu_array(), N, 0,
                   int(n * 11 / 10) - 1)
     ROC1 = division(M, N)
     M = diff(close_arr, int(n * 14 / 10) - 1)
     N = shift(close_arr, int(n * 14 / 10) - 1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), M, 0,
+    port_mask_nan(asset_indicator.to_gpu_array(), M, 0,
                   int(n * 14 / 10) - 1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), N, 0,
+    port_mask_nan(asset_indicator.to_gpu_array(), N, 0,
                   int(n * 14 / 10) - 1)
     ROC2 = division(M, N)
     Copp = PEwm(n, summation(ROC1, ROC2), asset_indicator).mean()
-    return cudf.Series(Copp)
+    return cudf.Series(Copp, nan_as_null=False)
 
 
 def accumulation_distribution(high_arr, low_arr, close_arr, vol_arr, n):
@@ -1054,7 +1067,7 @@ def accumulation_distribution(high_arr, low_arr, close_arr, vol_arr, n):
     ad = (2.0 * close_arr - high_arr - low_arr)/(high_arr - low_arr) * vol_arr
     M = diff(ad, n-1)
     N = shift(ad, n-1)
-    return cudf.Series(division(M, N))
+    return cudf.Series(division(M, N), nan_as_null=False)
 
 
 def port_accumulation_distribution(asset_indicator, high_arr,
@@ -1071,10 +1084,10 @@ def port_accumulation_distribution(asset_indicator, high_arr,
     """
     ad = (2.0 * close_arr - high_arr - low_arr)/(high_arr - low_arr) * vol_arr
     M = diff(ad, n-1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), M, 0, n - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), M, 0, n - 1)
     N = shift(ad, n-1)
-    port_mask_nan(asset_indicator.data.to_gpu_array(), N, 0, n - 1)
-    return cudf.Series(division(M, N))
+    port_mask_nan(asset_indicator.to_gpu_array(), N, 0, n - 1)
+    return cudf.Series(division(M, N), nan_as_null=False)
 
 
 def commodity_channel_index(high_arr, low_arr, close_arr, n):
@@ -1086,13 +1099,13 @@ def commodity_channel_index(high_arr, low_arr, close_arr, n):
     :param n: time steps
     :return: Commodity Channel Index in cudf.Series
     """
-    PP = average_price(high_arr.data.to_gpu_array(),
-                       low_arr.data.to_gpu_array(),
-                       close_arr.data.to_gpu_array())
+    PP = average_price(high_arr.to_gpu_array(),
+                       low_arr.to_gpu_array(),
+                       close_arr.to_gpu_array())
     M = Rolling(n, PP).mean()
     N = Rolling(n, PP).std()
     CCI = division(substract(PP, M), N)
-    return cudf.Series(CCI)
+    return cudf.Series(CCI, nan_as_null=False)
 
 
 def port_commodity_channel_index(asset_indicator, high_arr,
@@ -1106,12 +1119,12 @@ def port_commodity_channel_index(asset_indicator, high_arr,
     :param n: time steps
     :return: Commodity Channel Index in cudf.Series
     """
-    PP = average_price(high_arr.data.to_gpu_array(),
-                       low_arr.data.to_gpu_array(),
-                       close_arr.data.to_gpu_array())
+    PP = average_price(high_arr.to_gpu_array(),
+                       low_arr.to_gpu_array(),
+                       close_arr.to_gpu_array())
     M = Rolling(n, PP).mean()
-    port_mask_nan(asset_indicator.data.to_gpu_array(), M, 0, n - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), M, 0, n - 1)
     N = Rolling(n, PP).std()
-    port_mask_nan(asset_indicator.data.to_gpu_array(), N, 0, n - 1)
+    port_mask_nan(asset_indicator.to_gpu_array(), N, 0, n - 1)
     CCI = division(substract(PP, M), N)
-    return cudf.Series(CCI)
+    return cudf.Series(CCI, nan_as_null=False)
diff --git a/gquant/cuindicator/pewm.py b/gquant/cuindicator/pewm.py
index e353df8e..059dfbd3 100755
--- a/gquant/cuindicator/pewm.py
+++ b/gquant/cuindicator/pewm.py
@@ -48,8 +48,8 @@ def kernel(asset_indicator, in_arr, out_arr, average_length, span, arr_len,
         for j in range(0, average_length - 1, block_size):
             if (((tx + j) < average_length - 1) and
                     (starting_id - average_length + 1 + tx + j >= 0)):
-                        shared[tx + j] = \
-                            in_arr[starting_id - average_length + 1 + tx + j]
+                shared[tx + j] = \
+                    in_arr[starting_id - average_length + 1 + tx + j]
         cuda.syncthreads()
         # slice the shared memory for each threads
         start_shared = tx * thread_tile
@@ -94,7 +94,7 @@ def __init__(self, span, input_arr, asset_indicator, min_periods=None,
         if isinstance(input_arr, numba.cuda.cudadrv.devicearray.DeviceNDArray):
             self.gpu_in = input_arr
         else:
-            self.gpu_in = input_arr.data.to_gpu_array()
+            self.gpu_in = input_arr.to_gpu_array()
         if min_periods is None:
             self.min_periods = span
         else:
@@ -114,7 +114,7 @@ def __init__(self, span, input_arr, asset_indicator, min_periods=None,
                       numba.cuda.cudadrv.devicearray.DeviceNDArray):
             self.asset_indicator = asset_indicator
         else:
-            self.asset_indicator = asset_indicator.data.to_gpu_array()
+            self.asset_indicator = asset_indicator.to_gpu_array()
 
     def apply(self, method):
         gpu_out = numba.cuda.device_array_like(self.gpu_in)
diff --git a/gquant/cuindicator/rolling.py b/gquant/cuindicator/rolling.py
index 92f9d648..602291f1 100755
--- a/gquant/cuindicator/rolling.py
+++ b/gquant/cuindicator/rolling.py
@@ -108,7 +108,7 @@ def __init__(self, window, input_arr, min_periods=None, forward_window=0,
         if isinstance(input_arr, numba.cuda.cudadrv.devicearray.DeviceNDArray):
             self.gpu_in = input_arr
         else:
-            self.gpu_in = input_arr.data.to_gpu_array()
+            self.gpu_in = input_arr.to_gpu_array()
         if min_periods is None:
             self.min_periods = window + forward_window
         else:
@@ -128,7 +128,7 @@ def __init__(self, window, input_arr, min_periods=None, forward_window=0,
 
     def apply(self, method):
         gpu_out = numba.cuda.device_array_like(self.gpu_in)
-        # gpu_out = cudf.Series(gpu_out)
+        # gpu_out = cudf.Series(gpu_out, nan_as_null=False)
         kernel = get_rolling_kernel(method)
         kernel[(self.number_of_blocks,),
                (self.number_of_threads,),
diff --git a/gquant/dataframe_flow/_node_flow.py b/gquant/dataframe_flow/_node_flow.py
index bb057cfd..3eac2a43 100644
--- a/gquant/dataframe_flow/_node_flow.py
+++ b/gquant/dataframe_flow/_node_flow.py
@@ -674,6 +674,7 @@ def get_pout(out_dict, port):
 
     def decorate_process(self):
         import time
+
         def timer(*argv):
             start = time.time()
             result = self.process(*argv)
@@ -685,23 +686,21 @@ def timer(*argv):
         else:
             return self.process
 
-
     def __call__(self, inputs_data):
-        if self._using_ports():
-            # nodes with ports take dictionary as inputs
-            inputs = {iport: self.__make_copy(data_input)
-                      for iport, data_input in inputs_data.items()}
-        else:
-            # nodes without ports take list as inputs
-            inputs = [self.__make_copy(data_input)
-                      for data_input in inputs_data.values()]
-
         if self.load:
             if isinstance(self.load, bool):
                 output_df = self.load_cache()
             else:
                 output_df = self.load
         else:
+            if self._using_ports():
+                # nodes with ports take dictionary as inputs
+                inputs = {iport: self.__make_copy(data_input)
+                          for iport, data_input in inputs_data.items()}
+            else:
+                # nodes without ports take list as inputs
+                inputs = [self.__make_copy(inputs_data[ient['to_port']])
+                          for ient in self.inputs]
             if not self.delayed_process:
                 output_df = self.decorate_process()(inputs)
             else:
diff --git a/gquant/dataframe_flow/node.py b/gquant/dataframe_flow/node.py
index 8406a5f2..cdecefb3 100644
--- a/gquant/dataframe_flow/node.py
+++ b/gquant/dataframe_flow/node.py
@@ -113,7 +113,7 @@ def __init__(self, task):
         self.delayed_process = False
         # customized the column setup
         self.columns_setup()
-        self.profile = False # by default, do not profile
+        self.profile = False  # by default, do not profile
 
         if self._using_ports():
             PortsSpecSchema.validate_ports(self.ports_setup())
@@ -310,7 +310,7 @@ def load_cache(self, filename=None):
                         raise Exception(
                             'The task "{}" port "{}" key "{}" not found in '
                             'the hdf file "{}". Cannot load from cache.'
-                            .format(self.uid, oport, filename)
+                            .format(self.uid, oport, key, filename)
                         )
                     if cudf.DataFrame not in ptype:
                         warnings.warn(
diff --git a/gquant/plugin_nodes/__init__.py b/gquant/plugin_nodes/__init__.py
index ea149f38..7f30da6e 100644
--- a/gquant/plugin_nodes/__init__.py
+++ b/gquant/plugin_nodes/__init__.py
@@ -1,6 +1,6 @@
-from .dataloader import *  # noqa: F403
-from .analysis import *    # noqa: F403
-from .transform import *   # noqa: F403
-from .backtest import *    # noqa: F403
-from .strategy import *    # noqa: F403
-from .portofolio import *  # noqa: F403
+from .dataloader import *  # noqa: F403,F401
+from .analysis import *    # noqa: F403,F401
+from .transform import *   # noqa: F403,F401
+from .backtest import *    # noqa: F403,F401
+from .strategy import *    # noqa: F403,F401
+from .portofolio import *  # noqa: F403,F401
diff --git a/gquant/plugin_nodes/backtest/simpleBackTest.py b/gquant/plugin_nodes/backtest/simpleBackTest.py
index df127c07..2a6bd4ed 100644
--- a/gquant/plugin_nodes/backtest/simpleBackTest.py
+++ b/gquant/plugin_nodes/backtest/simpleBackTest.py
@@ -25,6 +25,7 @@ def process(self, inputs):
         input_df['strategy_returns'] = input_df['signal'] * input_df['returns']
         return input_df
 
+
 if __name__ == "__main__":
     from gquant.dataloader.csvStockLoader import CsvStockLoader
     from gquant.transform.assetFilterNode import AssetFilterNode
diff --git a/gquant/plugin_nodes/dataloader/csvStockLoader.py b/gquant/plugin_nodes/dataloader/csvStockLoader.py
index a9990942..88753abc 100644
--- a/gquant/plugin_nodes/dataloader/csvStockLoader.py
+++ b/gquant/plugin_nodes/dataloader/csvStockLoader.py
@@ -1,6 +1,5 @@
 from gquant.dataframe_flow import Node
 import cudf
-import pandas as pd
 
 
 class CsvStockLoader(Node):
@@ -29,18 +28,19 @@ def process(self, inputs):
         -------
         cudf.DataFrame
         """
-
-        df = pd.read_csv(self.conf['path'],
-                         converters={'DTE': lambda x: pd.Timestamp(str(x))})
-        df = df[['DTE', 'OPEN',
-                 'CLOSE', 'HIGH',
-                 'LOW', 'SM_ID', 'VOLUME']]
+        df = cudf.read_csv(self.conf['path'])
+        # extract the year, month, day
+        ymd = df['DTE'].astype('str').str.extract(r'(\d\d\d\d)(\d\d)(\d\d)')
+        # construct the standard datetime str
+        df['DTE'] = ymd[0].str.cat(ymd[1],
+                                   '-').str.cat(ymd[2],
+                                                '-').astype('datetime64[ms]')
+        df = df[['DTE', 'OPEN', 'CLOSE', 'HIGH', 'LOW', 'SM_ID', 'VOLUME']]
         df['VOLUME'] /= 1000
-        output = cudf.from_pandas(df)
         # change the names
-        output.columns = ['datetime', 'open', 'close', 'high',
-                          'low', "asset", 'volume']
-        return output
+        df.columns = ['datetime', 'open', 'close',
+                      'high', 'low', "asset", 'volume']
+        return df
 
 
 if __name__ == "__main__":
diff --git a/gquant/plugin_nodes/strategy/movingAverageStrategyNode.py b/gquant/plugin_nodes/strategy/movingAverageStrategyNode.py
index 3e6b0954..ab06d7ab 100644
--- a/gquant/plugin_nodes/strategy/movingAverageStrategyNode.py
+++ b/gquant/plugin_nodes/strategy/movingAverageStrategyNode.py
@@ -3,6 +3,7 @@
 from numba import cuda
 import math
 import numpy as np
+import cudf
 
 
 @cuda.jit
@@ -22,9 +23,9 @@ def moving_average_signal_kernel(ma_fast, ma_slow, out_arr, arr_len):
 
 def moving_average_signal(stock_df, n_fast, n_slow):
     ma_slow = ci.moving_average(stock_df['close'],
-                                n_slow).data.to_gpu_array()
+                                n_slow).to_gpu_array()
     ma_fast = ci.moving_average(stock_df['close'],
-                                n_fast).data.to_gpu_array()
+                                n_fast).to_gpu_array()
     out_arr = cuda.device_array_like(ma_fast)
     array_len = len(ma_slow)
     number_of_threads = 256
@@ -69,6 +70,9 @@ def process(self, inputs):
         n_fast = self.conf['fast']
         n_slow = self.conf['slow']
         signal, slow, fast = moving_average_signal(input_df, n_fast, n_slow)
+        signal = cudf.Series(signal, index=input_df.index)
+        slow = cudf.Series(slow, index=input_df.index)
+        fast = cudf.Series(fast, index=input_df.index)
         input_df['signal'] = signal
         input_df['ma_slow'] = slow
         input_df['ma_slow'] = input_df['ma_slow'].fillna(0.0)
diff --git a/gquant/plugin_nodes/strategy/portExpMovingAverageStrategyNode.py b/gquant/plugin_nodes/strategy/portExpMovingAverageStrategyNode.py
index 545468a6..ca4ba63b 100644
--- a/gquant/plugin_nodes/strategy/portExpMovingAverageStrategyNode.py
+++ b/gquant/plugin_nodes/strategy/portExpMovingAverageStrategyNode.py
@@ -5,6 +5,7 @@
 from functools import partial
 import math
 import numpy as np
+import cudf
 
 
 @cuda.jit
@@ -25,10 +26,10 @@ def moving_average_signal_kernel(ma_fast, ma_slow, out_arr, arr_len):
 def port_exponential_moving_average(stock_df, n_fast, n_slow):
     ma_slow = ci.port_exponential_moving_average(stock_df['indicator'],
                                                  stock_df['close'],
-                                                 n_slow).data.to_gpu_array()
+                                                 n_slow).to_gpu_array()
     ma_fast = ci.port_exponential_moving_average(stock_df['indicator'],
                                                  stock_df['close'],
-                                                 n_fast).data.to_gpu_array()
+                                                 n_fast).to_gpu_array()
     out_arr = cuda.device_array_like(ma_fast)
     number_of_threads = 256
     array_len = len(stock_df)
@@ -79,6 +80,9 @@ def process(self, inputs):
         signal, slow, fast = port_exponential_moving_average(input_df,
                                                              n_fast,
                                                              n_slow)
+        signal = cudf.Series(signal, index=input_df.index)
+        slow = cudf.Series(slow, index=input_df.index)
+        fast = cudf.Series(fast, index=input_df.index)
         input_df['signal'] = signal
         input_df['exp_ma_slow'] = slow
         input_df['exp_ma_slow'] = input_df['exp_ma_slow'].fillna(0.0)
diff --git a/gquant/plugin_nodes/strategy/xgboostStrategyNode.py b/gquant/plugin_nodes/strategy/xgboostStrategyNode.py
index f6a9adbb..4a5c8262 100644
--- a/gquant/plugin_nodes/strategy/xgboostStrategyNode.py
+++ b/gquant/plugin_nodes/strategy/xgboostStrategyNode.py
@@ -25,7 +25,7 @@ def signal_kernel(signal_arr, out_arr, arr_len):
 
 
 def compute_signal(signal):
-    signal_arr = signal.data.to_gpu_array()
+    signal_arr = signal.to_gpu_array()
     out_arr = cuda.device_array_like(signal_arr)
     number_of_threads = 256
     array_len = len(signal)
@@ -81,27 +81,13 @@ def process(self, inputs):
         dataframe
         """
         dxgb_params = {
-                'nround':            100,
                 'max_depth':         8,
                 'max_leaves':        2 ** 8,
-                'alpha':             0.9,
-                'eta':               0.1,
-                'gamma':             0.1,
-                'learning_rate':     0.1,
-                'subsample':         1,
-                'reg_lambda':        1,
-                'scale_pos_weight':  2,
-                'min_child_weight':  30,
                 'tree_method':       'gpu_hist',
-                'distributed_dask':  True,
-                'loss':              'ls',
-                # 'objective':         'gpu:reg:linear',
                 'objective':         'reg:squarederror',
-                'max_features':      'auto',
-                'criterion':         'friedman_mse',
                 'grow_policy':       'lossguide',
-                'verbose':           True
         }
+        num_of_rounds = 100
         if 'xgboost_parameters' in self.conf:
             dxgb_params.update(self.conf['xgboost_parameters'])
         input_df = inputs[0]
@@ -117,11 +103,13 @@ def process(self, inputs):
         target = model_df[self.conf['target']]
         dmatrix = xgb.DMatrix(train, label=target)
         bst = xgb.train(dxgb_params, dmatrix,
-                        num_boost_round=dxgb_params['nround'])
+                        num_boost_round=num_of_rounds)
         # make inferences
         infer_dmatrix = xgb.DMatrix(input_df[train_cols])
-        prediction = cudf.Series(bst.predict(infer_dmatrix)).astype('float64')
+        prediction = cudf.Series(bst.predict(infer_dmatrix),
+                                 nan_as_null=False).astype('float64')
         signal = compute_signal(prediction)
+        signal = cudf.Series(signal, index=input_df.index)
         input_df['signal'] = signal
         # remove the bad datapints
         input_df = input_df.dropna()
diff --git a/gquant/plugin_nodes/transform/assetIndicatorNode.py b/gquant/plugin_nodes/transform/assetIndicatorNode.py
index d31b2237..54df7eb8 100644
--- a/gquant/plugin_nodes/transform/assetIndicatorNode.py
+++ b/gquant/plugin_nodes/transform/assetIndicatorNode.py
@@ -39,11 +39,9 @@ def process(self, inputs):
         """
 
         input_df = inputs[0]
-        input_df = input_df.groupby(["asset"], method='cudf') \
-            .apply_grouped(indicator_fun,
-                           incols=[],
-                           outcols={'indicator': 'int32'},
-                           tpb=256)
+        input_df['indicator'] = (input_df['asset'] -
+                                 input_df['asset'].shift(1)).fillna(1)
+        input_df['indicator'] = (input_df['indicator'] != 0).astype('int32')
         return input_df
 
 
diff --git a/gquant/plugin_nodes/transform/indicatorNode.py b/gquant/plugin_nodes/transform/indicatorNode.py
index 0094c8b1..9fac56c1 100644
--- a/gquant/plugin_nodes/transform/indicatorNode.py
+++ b/gquant/plugin_nodes/transform/indicatorNode.py
@@ -62,16 +62,19 @@ def process(self, inputs):
             if isinstance(v, tuple) and 'outputs' in indicator:
                 for out in indicator['outputs']:
                     out_col = self._compose_name(indicator, [out])
-                    input_df[out_col] = getattr(v, out)
+                    val = getattr(v, out)
+                    val.index = input_df.index
+                    input_df[out_col] = val
                     # out_cols.append(out_col)
             else:
                 if isinstance(v, tuple):
                     v = v[0]
                 out_col = self._compose_name(indicator, [])
+                v.index = input_df.index
                 input_df[out_col] = v
         # remove all the na elements, requires cudf>=0.8
         if "remove_na" in self.conf and self.conf["remove_na"]:
-            input_df = input_df.dropna()
+            input_df = input_df.nans_to_nulls().dropna()
         return input_df
 
 
diff --git a/gquant/plugin_nodes/transform/returnFeatureNode.py b/gquant/plugin_nodes/transform/returnFeatureNode.py
index 44182131..0d5b6998 100644
--- a/gquant/plugin_nodes/transform/returnFeatureNode.py
+++ b/gquant/plugin_nodes/transform/returnFeatureNode.py
@@ -1,11 +1,11 @@
 from gquant.dataframe_flow import Node
-import gquant.cuindicator as ci
 from numba import cuda
 import numpy as np
 
 
-def mask_returns(indicator):
-    for i in range(cuda.threadIdx.x, indicator.size, cuda.blockDim.x):
+def mask_returns(close, indicator):
+    # print(len(close), cuda.threadIdx.x, cuda.blockDim.x, len(indicator))
+    for i in range(cuda.threadIdx.x, len(close), cuda.blockDim.x):
         if i == 0:
             indicator[i] = 1
         else:
@@ -40,14 +40,14 @@ def process(self, inputs):
         dataframe
         """
         input_df = inputs[0]
-        input_df['returns'] = ci.rate_of_change(input_df['close'], 2) \
-            .fillna(0.0)
-        input_df = input_df.groupby(["asset"], method='cudf') \
-            .apply_grouped(mask_returns,
-                           incols=[],
-                           outcols={'indicator': 'int32'},
-                           tpb=256)
-        return input_df.query('indicator == 0 ').drop('indicator')
+        shifted = input_df['close'].shift(1)
+        input_df['returns'] = (input_df['close'] - shifted) / shifted
+        input_df['returns'] = input_df['returns'].fillna(0.0)
+        input_df['indicator'] = (input_df['asset'] -
+                                 input_df['asset'].shift(1)).fillna(1)
+        input_df['indicator'] = (input_df['indicator'] != 0).astype('int32')
+        input_df['indicator'][input_df['indicator'] == 1] = None
+        return input_df.dropna(subset=['indicator']).drop('indicator')
 
 
 class CpuReturnFeatureNode(ReturnFeatureNode):
diff --git a/notebooks/01_tutorial.ipynb b/notebooks/01_tutorial.ipynb
index 43f4fe13..ac630d15 100644
--- a/notebooks/01_tutorial.ipynb
+++ b/notebooks/01_tutorial.ipynb
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -202,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -259,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -302,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -338,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -347,17 +347,17 @@
      "text": [
       "Output of build task graph are instances of each task in a dictionary:\n",
       "\n",
-      "load_csv_data: <NodeInTaskGraph gquant.plugin_nodes.dataloader.csvStockLoader.CsvStockLoader object at 0x7f2581c22358>\n",
-      "min_volume: <NodeInTaskGraph gquant.plugin_nodes.transform.volumeFilterNode.VolumeFilterNode object at 0x7f2581a1a240>\n",
-      "sort: <NodeInTaskGraph gquant.plugin_nodes.transform.sortNode.SortNode object at 0x7f25809908d0>\n",
-      "add_return: <NodeInTaskGraph gquant.plugin_nodes.transform.returnFeatureNode.ReturnFeatureNode object at 0x7f2580990ac8>\n",
-      "stock_symbol: <NodeInTaskGraph gquant.plugin_nodes.dataloader.stockNameLoader.StockNameLoader object at 0x7f2580990cc0>\n",
-      "volume_mean: <NodeInTaskGraph gquant.plugin_nodes.transform.averageNode.AverageNode object at 0x7f2580990eb8>\n",
-      "return_mean: <NodeInTaskGraph gquant.plugin_nodes.transform.averageNode.AverageNode object at 0x7f25809a40f0>\n",
-      "left_merge_1: <NodeInTaskGraph gquant.plugin_nodes.transform.leftMergeNode.LeftMergeNode object at 0x7f25809a42e8>\n",
-      "left_merge_2: <NodeInTaskGraph gquant.plugin_nodes.transform.leftMergeNode.LeftMergeNode object at 0x7f25809a44e0>\n",
-      "output_csv_1: <NodeInTaskGraph gquant.plugin_nodes.analysis.outCsvNode.OutCsvNode object at 0x7f25809a46d8>\n",
-      "output_csv_2: <NodeInTaskGraph gquant.plugin_nodes.analysis.outCsvNode.OutCsvNode object at 0x7f25809a48d0>\n",
+      "load_csv_data: <NodeInTaskGraph gquant.plugin_nodes.dataloader.csvStockLoader.CsvStockLoader object at 0x7fedff3fb898>\n",
+      "min_volume: <NodeInTaskGraph gquant.plugin_nodes.transform.volumeFilterNode.VolumeFilterNode object at 0x7fedff439390>\n",
+      "sort: <NodeInTaskGraph gquant.plugin_nodes.transform.sortNode.SortNode object at 0x7fedf9c1c1d0>\n",
+      "add_return: <NodeInTaskGraph gquant.plugin_nodes.transform.returnFeatureNode.ReturnFeatureNode object at 0x7fedf9c1c390>\n",
+      "stock_symbol: <NodeInTaskGraph gquant.plugin_nodes.dataloader.stockNameLoader.StockNameLoader object at 0x7fedf9c1c588>\n",
+      "volume_mean: <NodeInTaskGraph gquant.plugin_nodes.transform.averageNode.AverageNode object at 0x7fedf9c1c780>\n",
+      "return_mean: <NodeInTaskGraph gquant.plugin_nodes.transform.averageNode.AverageNode object at 0x7fedf9c1c978>\n",
+      "left_merge_1: <NodeInTaskGraph gquant.plugin_nodes.transform.leftMergeNode.LeftMergeNode object at 0x7fedf9c1cb70>\n",
+      "left_merge_2: <NodeInTaskGraph gquant.plugin_nodes.transform.leftMergeNode.LeftMergeNode object at 0x7fedf9c1cd68>\n",
+      "output_csv_1: <NodeInTaskGraph gquant.plugin_nodes.analysis.outCsvNode.OutCsvNode object at 0x7fedf9c1cf60>\n",
+      "output_csv_2: <NodeInTaskGraph gquant.plugin_nodes.analysis.outCsvNode.OutCsvNode object at 0x7fedf9c2e198>\n",
       "\n"
      ]
     }
@@ -373,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -396,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -446,24 +446,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "id:load_csv_data process time:56.329\n",
-      "id:min_volume process time:0.134\n",
-      "id:sort process time:0.155\n",
-      "id:add_return process time:0.186\n",
-      "id:volume_mean process time:0.025\n",
-      "id:return_mean process time:0.027\n",
-      "id:stock_symbol process time:0.013\n",
-      "id:left_merge_1 process time:0.008\n",
-      "id:output_csv_1 process time:0.013\n",
-      "id:left_merge_2 process time:0.005\n",
-      "id:output_csv_2 process time:0.013\n"
+      "id:load_csv_data process time:3.874s\n",
+      "id:min_volume process time:0.199s\n",
+      "id:sort process time:0.125s\n",
+      "id:add_return process time:0.221s\n",
+      "id:volume_mean process time:0.050s\n",
+      "id:return_mean process time:0.045s\n",
+      "id:stock_symbol process time:0.015s\n",
+      "id:left_merge_1 process time:0.003s\n",
+      "id:output_csv_1 process time:0.020s\n",
+      "id:left_merge_2 process time:0.003s\n",
+      "id:output_csv_2 process time:0.020s\n"
      ]
     }
    ],
@@ -481,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -494,63 +494,13 @@
       "1     869589   110.456066       DSLV\n",
       "2     869590    66.607253       BPTH\n",
       "3     869592    56.041766         SP\n",
-      "4      22252   504.761396        CEF\n",
-      "5      22254    66.178077       SKYY\n",
-      "6      22260   401.527545       CLDX\n",
-      "7      22262   536.560685       UNIS\n",
-      "8      22266  1395.477945        PLD\n",
-      "9      22281  2942.558898       SQQQ\n",
-      "10     22283   824.567980        HCN\n",
-      "11     22284    92.215897        CIK\n",
-      "12     22293   328.522166        FSP\n",
-      "13     22294   518.653193       ECYT\n",
-      "14     22303    73.952609       SUMR\n",
-      "15     22304   323.398782        PMC\n",
-      "16     22306  1188.195338       MTOR\n",
-      "17     22312   328.723606         RP\n",
-      "18     22316   845.681667       MTDR\n",
-      "19     22323  2589.050216        CIE\n",
-      "20     22338   918.747811       ROVI\n",
-      "21     22339   734.596191         NM\n",
-      "22     22348   601.541333       SYRG\n",
-      "23     22352   261.406284       SYMX\n",
-      "24     22355   393.671418       ACRX\n",
-      "25     22356  4343.659885         GG\n",
-      "26     22361   102.828032        BTX\n",
-      "27     22363  2082.957392        MSI\n",
-      "28     22364  2030.574212       SWFT\n",
-      "29     22370   260.718094       ARWR\n",
+      "4     869349    91.161991       VIIX\n",
       "...      ...          ...        ...\n",
-      "3654   24022    80.863700       FULL\n",
-      "3655   24029   206.696661         JE\n",
-      "3656   24030   123.923580       TOWR\n",
-      "3657   24031   160.319024        HHC\n",
-      "3658   24032   125.691977        DPG\n",
-      "3659   24036   480.417818       JMBA\n",
-      "3660   24038   159.318593       HTHT\n",
-      "3661   24040   163.879043        TST\n",
-      "3662   24041    95.472417        PTN\n",
-      "3663   24046   128.900185        NTN\n",
-      "3664   24051   592.212903       POST\n",
-      "3665   24053    78.732765       AOSL\n",
-      "3666   24059    54.465283        HEQ\n",
-      "3667   24065  1215.237406       SMFG\n",
-      "3668   24066   622.342256       MEMP\n",
-      "3669   24067   252.632282        AXU\n",
-      "3670   24069  1087.383937        DLR\n",
-      "3671   24072   233.026579        BBN\n",
-      "3672   24074   549.153817        BKU\n",
-      "3673   24076   295.160203        STV\n",
-      "3674   24077  1996.519771       INVN\n",
-      "3675   24078   147.643035        TCO\n",
-      "3676   24088    86.557899        MCF\n",
-      "3677   24100   146.701113       XOXO\n",
-      "3678   24108   177.371443       PRIM\n",
-      "3679   24112   626.713313       CLNY\n",
-      "3680   24114   444.717606        NSU\n",
-      "3681   24118  5990.215130         GS\n",
-      "3682   24121   221.881592        SMA\n",
-      "3683   24122   737.811219       ULTA\n",
+      "3679    5890  1386.894587        DRI\n",
+      "3680    5891   164.916612        DRL\n",
+      "3681    5893   336.161817        DRQ\n",
+      "3682    5896   453.901682        DSL\n",
+      "3683    5897    82.365824        DSM\n",
       "\n",
       "[3684 rows x 3 columns]\n",
       "\n",
@@ -561,62 +511,12 @@
       "2     869590  0.005321       BPTH\n",
       "3     869592  0.000502         SP\n",
       "4     708893 -0.000588        UCP\n",
-      "5     708921  0.000156       USAC\n",
-      "6     708931  0.000799       USPH\n",
-      "7     708953 -0.000139       VEEV\n",
-      "8     708964 -0.001573       VJET\n",
-      "9     708967  0.000819       VLRS\n",
-      "10    708973 -0.003376       VMEM\n",
-      "11    708986  0.000715       VOYA\n",
-      "12    709003  0.009236        WAC\n",
-      "13    709005  0.001957       WAGE\n",
-      "14    709016  0.000328       WCIC\n",
-      "15    709019  0.000788       WDAY\n",
-      "16    709024  0.000875        WEX\n",
-      "17    709038  0.000875        WGP\n",
-      "18    709047 -0.000371        WLH\n",
-      "19    709054 -0.000588        WMC\n",
-      "20    709061  0.000213       WNRL\n",
-      "21    709082  0.000187        WSR\n",
-      "22    709090  0.001853       WUBA\n",
-      "23    709091  0.001273       WWAV\n",
-      "24    709112  0.001282        XON\n",
-      "25    709114  0.002133        XPO\n",
-      "26    709127 -0.000696       YUME\n",
-      "27    709143  0.000624        ZTS\n",
-      "28    767696  0.001142       CANF\n",
-      "29    795074  0.000044       NVGS\n",
       "...      ...       ...        ...\n",
-      "3654   22418  0.000232       NWSA\n",
-      "3655   22431  0.003480        NCT\n",
-      "3656   22433  0.000768         MD\n",
-      "3657   22436  0.000234        NLY\n",
-      "3658   22437  0.000011       VGSH\n",
-      "3659   22440  0.000463        EQR\n",
-      "3660   22444  0.000490       DNKN\n",
-      "3661   22449  0.001112        LYB\n",
-      "3662   22450  0.001024         KS\n",
-      "3663   22451  0.000637       LSTR\n",
-      "3664   22456 -0.000242       RBCN\n",
-      "3665   22457 -0.000096        CVE\n",
-      "3666   22459  0.000232        CUR\n",
-      "3667   22460  0.001203        VAC\n",
-      "3668   22461 -0.000239         MY\n",
-      "3669   22463  0.000429        NAK\n",
-      "3670   22465  0.001771        NAV\n",
-      "3671   22474  0.000002       ACWX\n",
-      "3672   22476  0.000342        PEI\n",
-      "3673   22477  0.000309         HI\n",
-      "3674   22481  0.001255        SRV\n",
-      "3675   22486  0.000533        THR\n",
-      "3676   22492  0.000203        RLJ\n",
-      "3677   22494  0.000740       BRFS\n",
-      "3678   22499  0.002204        LNG\n",
-      "3679   22500  0.000173        ANH\n",
-      "3680   22503  0.022120       VRML\n",
-      "3681   22505  0.000514        GNE\n",
-      "3682   22507  0.000102         ZN\n",
-      "3683   22508  0.001179       AXAS\n",
+      "3679   23748  0.001471       FBHS\n",
+      "3680   23750 -0.000059        BUI\n",
+      "3681   23752  0.006837       TEAR\n",
+      "3682   23755  0.000506        PUK\n",
+      "3683   23762  0.003529       TPLM\n",
       "\n",
       "[3684 rows x 3 columns]\n"
      ]
@@ -641,7 +541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -650,8 +550,8 @@
      "text": [
       "\n",
       "csv files created:\n",
-      "./symbol_volume.csv\n",
-      "./symbol_returns.csv\n"
+      "./symbol_returns.csv\n",
+      "./symbol_volume.csv\n"
      ]
     }
    ],
@@ -677,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -690,57 +590,7 @@
       "2       1404   2073.529168\n",
       "3       1544     80.659223\n",
       "4       1545  18922.826861\n",
-      "5       1551    136.904912\n",
-      "6       1556    255.454874\n",
-      "7       1562    185.359122\n",
-      "8       1565     66.379480\n",
-      "9       1568    948.050928\n",
-      "10      1570   2026.381315\n",
-      "11      1571    104.924382\n",
-      "12      1576     97.045198\n",
-      "13      1578    544.175453\n",
-      "14      1580   1388.079015\n",
-      "15      1581    311.846330\n",
-      "16      1583    980.849498\n",
-      "17      1586    624.764849\n",
-      "18      1587     65.537620\n",
-      "19      1589    335.959602\n",
-      "20      1592    126.559431\n",
-      "21      1595    523.585610\n",
-      "22      1597     69.508564\n",
-      "23      1598   5667.927834\n",
-      "24      1609    128.542941\n",
-      "25      1611   2492.002047\n",
-      "26      1614    888.784263\n",
-      "27      1619    390.552658\n",
-      "28      1625    267.423016\n",
-      "29      1626     52.514382\n",
       "...      ...           ...\n",
-      "3654  869489    100.866069\n",
-      "3655  869492    138.699315\n",
-      "3656  869497     47.834918\n",
-      "3657  869499    266.137727\n",
-      "3658  869502     47.732030\n",
-      "3659  869504     73.639532\n",
-      "3660  869509   1700.065794\n",
-      "3661  869510    432.583828\n",
-      "3662  869511    223.035926\n",
-      "3663  869517    181.131818\n",
-      "3664  869527     50.513043\n",
-      "3665  869532    122.329259\n",
-      "3666  869533   8766.409936\n",
-      "3667  869535    192.054983\n",
-      "3668  869539    127.730000\n",
-      "3669  869541   2002.252055\n",
-      "3670  869543    346.725925\n",
-      "3671  869544    137.655093\n",
-      "3672  869546    215.816984\n",
-      "3673  869551    225.498205\n",
-      "3674  869554    867.615686\n",
-      "3675  869557    110.977143\n",
-      "3676  869558   1120.287506\n",
-      "3677  869567    237.873039\n",
-      "3678  869571    639.127042\n",
       "3679  869577    147.814845\n",
       "3680  869584    673.625235\n",
       "3681  869589    110.456066\n",
@@ -790,7 +640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -805,57 +655,7 @@
       "2       1404  0.000423\n",
       "3       1544  0.001153\n",
       "4       1545  0.000784\n",
-      "5       1551  0.001066\n",
-      "6       1556  0.000403\n",
-      "7       1562  0.001368\n",
-      "8       1565  0.001526\n",
-      "9       1568  0.002258\n",
-      "10      1570  0.000571\n",
-      "11      1571  0.000547\n",
-      "12      1576  0.000503\n",
-      "13      1578  0.000487\n",
-      "14      1580  0.001403\n",
-      "15      1581  0.000729\n",
-      "16      1583  0.000677\n",
-      "17      1586  0.001734\n",
-      "18      1587  0.000718\n",
-      "19      1589  0.001130\n",
-      "20      1592 -0.000290\n",
-      "21      1595  0.000554\n",
-      "22      1597  0.003095\n",
-      "23      1598  0.000850\n",
-      "24      1609  0.000227\n",
-      "25      1611  0.000578\n",
-      "26      1614  0.000525\n",
-      "27      1619  0.001124\n",
-      "28      1625  0.026855\n",
-      "29      1626  0.001853\n",
       "...      ...       ...\n",
-      "3654  869489 -0.000925\n",
-      "3655  869492 -0.001543\n",
-      "3656  869497 -0.000736\n",
-      "3657  869499  0.000804\n",
-      "3658  869502 -0.000334\n",
-      "3659  869504  0.001255\n",
-      "3660  869509 -0.000324\n",
-      "3661  869510 -0.001525\n",
-      "3662  869511 -0.000452\n",
-      "3663  869517 -0.000348\n",
-      "3664  869527 -0.000537\n",
-      "3665  869532  0.000481\n",
-      "3666  869533  0.000932\n",
-      "3667  869535 -0.000536\n",
-      "3668  869539  0.001054\n",
-      "3669  869541  0.000303\n",
-      "3670  869543  0.000836\n",
-      "3671  869544  0.000700\n",
-      "3672  869546  0.000833\n",
-      "3673  869551  0.000842\n",
-      "3674  869554  0.016520\n",
-      "3675  869557  0.019985\n",
-      "3676  869558  0.000518\n",
-      "3677  869567 -0.001970\n",
-      "3678  869571 -0.002908\n",
       "3679  869577 -0.000276\n",
       "3680  869584  0.000369\n",
       "3681  869589  0.001077\n",
@@ -893,7 +693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -901,8 +701,8 @@
      "output_type": "stream",
      "text": [
       "Using in-memory dataframes for load:\n",
-      "CPU times: user 41.1 ms, sys: 9.03 ms, total: 50.1 ms\n",
-      "Wall time: 52.4 ms\n"
+      "CPU times: user 51 ms, sys: 804 µs, total: 51.8 ms\n",
+      "Wall time: 49.8 ms\n"
      ]
     }
    ],
@@ -918,7 +718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -926,8 +726,8 @@
      "output_type": "stream",
      "text": [
       "Using cached dataframes on disk for load:\n",
-      "CPU times: user 60 ms, sys: 4.99 ms, total: 65 ms\n",
-      "Wall time: 81.8 ms\n"
+      "CPU times: user 61 ms, sys: 716 µs, total: 61.7 ms\n",
+      "Wall time: 59.2 ms\n"
      ]
     }
    ],
@@ -943,7 +743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -951,8 +751,8 @@
      "output_type": "stream",
      "text": [
       "Re-running dataframes calculations instead of using load:\n",
-      "CPU times: user 996 ms, sys: 1.34 s, total: 2.33 s\n",
-      "Wall time: 7.55 s\n"
+      "CPU times: user 873 ms, sys: 691 ms, total: 1.56 s\n",
+      "Wall time: 1.63 s\n"
      ]
     }
    ],
@@ -974,15 +774,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 63.8 ms, sys: 4.41 ms, total: 68.2 ms\n",
-      "Wall time: 75.4 ms\n"
+      "CPU times: user 50.5 ms, sys: 8.23 ms, total: 58.8 ms\n",
+      "Wall time: 56.2 ms\n"
      ]
     }
    ],
@@ -1012,7 +812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1047,7 +847,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1105,6 +905,13 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1129,7 +936,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/02_single_stock_trade.ipynb b/notebooks/02_single_stock_trade.ipynb
index ae6355d8..75d4d90d 100644
--- a/notebooks/02_single_stock_trade.ipynb
+++ b/notebooks/02_single_stock_trade.ipynb
@@ -91,7 +91,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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TrVs34+vra5xOp0lNTTVbtmxpsZ3i4mKzePFik5iYaHx9fU1MTIxZtGiR+dWvfmVtd9CgQdb4zMxMM2PGDBMWFmYCAwPN4MGDzbp168z48eOt8TfddFOb5/PNvF26dDHz5s0zWVlZLcbt3bvXLF682Fx22WUmKCjIREZGmmHDhplnnnnGNDU1nXTbd999t5Fk9u3b1+Zc39SW+bf1s83IyDCjR482wcHBJjEx0axcudJ6ra3bssuaNWuMw+Ewf/vb3+yOAgD4PyscxrSzk9EAAOgg1qxZo9mzZ+uee+7Rww8/bHccoF3YsmWLpk2bpgULFugvf/mL3XEAAP9nJacYAABwjkyfPl3PPfecHn30Uf34xz9WXV2d3ZEAW73wwgu6+uqrNXPmTOuuEQCA9oOCAACAc2jhwoVav369Vq1apUGDBmnXrl12RwLOu6KiIi1cuFA/+tGPtHjxYr388svy9va2OxYA4BsoCAAAOMdSU1P16aefKj4+XiNHjtTixYvb9S0QzyeHw/Gdy/333293zAsmZ3v01ltvqU+fPtqyZYtWr16tJ598Ul5e/BcUANojrkEAAMB5YozRiy++qKVLlyokJES//OUvddNNNykgIMDuaMBZl5aWpgceeEA7duzQbbfdpkceeeR73cUCAHDOcQ0CAADOF4fDoUWLFunQoUOaMWOG7r77bnXv3l1/+MMfVF1dbXc84Hszxmj9+vUaPny4Jk2apODgYO3YsUN/+tOfKAcA4AJAQQAAwHkWGxurP/7xj/ryyy913XXX6d5771XXrl11zz33KDMz0+54QJuVlZVpxYoVuuKKKzRt2jR16tRJH330kTZs2KChQ4faHQ8AcJo4xQAAAJsVFRXpz3/+s5577jnl5uZq1KhRuvnmm3XttdcqKCjI7njASRlj9P777+uvf/2r3n77bXl7e2vu3Lm6/fbbNXDgQLvjAQDabiUFAQAA7URTU5M2bdqkv/71r3rnnXcUEBCgqVOnaubMmZoyZYqCg4PtjoiLXFNTk3bs2KHVq1frH//4h7766isNGTJEN910k+bNm6ewsDC7IwIAzhwFAQAA7VFRUZFee+01rVq1Sunp6fL19dXEiRM1c+ZM6xBu4Hyoq6vTtm3btGrVKq1Zs0aFhYVKSUnRzJkzdf3116tfv352RwQAnB0UBAAAtHcnTpzQunXrtG7dOr377ruqqalRr169NGrUKE2YMEGpqal8c4uzpqmpSZ9//rm2b9+uzZs3a+PGjaqoqFDv3r01Z84cTZs2TYMGDbI7JgDg7KMgAADgQlJZWam0tDRt3bpVW7du1aFDh+Tn56dhw4Zp3Lhx+n//7//pBz/4gUJDQ+2OigtEY2OjDh48qO3bt2vr1q365z//qRMnTqhz584aN26cxo0bp9TUVCUlJdkdFQBwblEQAABwIcvPz9eWLVuswiA7O1ve3t667LLLNHToUGvp06ePvL297Y6LduDYsWP66KOP9NFHH+njjz/W7t27VV1drbCwMI0ZM0bjxo3T+PHj1bdvXzkcDrvjAgDOHwoCAAA6kuPHj1s7fx999JE++eQTVVVVKTg4WAMGDFC/fv10+eWXq1+/furbt6+cTqfdkXGO1NfXKyMjQwcOHNCBAwe0f/9+ffrppyooKJC3t7f69OnTokS67LLLKJEA4OJGQQAAQEfm8Xj02WefadeuXdqzZ48OHDiggwcPqqKiQpLUtWtX9e3bV/369VPv3r116aWXqkePHoqKirI5OU5XTU2NvvjiCx0+fFhZWVnav3+/Dh48qIyMDDU0NMjX11e9evVSv379dMUVV2jIkCEaNGiQQkJC7I4OAGhfKAgAALgYHT16tMU3ywcPHtThw4dVX18vSYqIiFCPHj2swqBHjx7q3r27kpKSFBsbKy8vL5tncHEpKytTTk6Ojhw5osOHD1uFwBdffKHc3FwZY+RwOJSYmGgVPpdffrn69u2ryy67TL6+vnZPAQDQ/lEQAACAf/N4PDp27Ji149m8E3r48GF99dVXVnng6+ur+Ph4JSYmKikpSYmJiUpISFBSUpLi4+MVExOj6Oho+fn52Tyj9s8Yo+LiYhUXFys/P185OTk6duyYcnJylJOTo9zcXGVnZ6uqqkqS5HA4lJCQoEsvvdQqb77+OCAgwOYZAQAuYBQEAADgu3k8HuXm5ionJ0fZ2dnW46/vzJaWlrb4nfDwcKssiI6OVpcuXazHERERcjqdCg8Pb/HzQr77Qm1trVwul8rLy1v8LCsrs0qA4uJiFRYWqrCwUCUlJSouLpbH47G2ERgYqOTk5BalS1JSkhISEpSYmKhu3bpRAgAAzhUKAgAAcHZUV1crNze3xY5wUVGRiouLVVRUpIKCAhUXF6ukpERlZWVqaGhotQ1vb2+rMIiIiJCPj49CQ0Pl7++voKAgBQYGKiAgQMHBwfLz81NYWJh1Yb3mMd/kdDpbnRJRUVHRYsdckhoaGlRZWWk9d7lc8ng8Ki8vl8fjUUVFhTWmrq5O1dXVqqystIqA2trak34uERER6tSpk1WOxMTEKCYmxlrXXJw0rwMAwCYUBAAAwB7V1dWtvm1vfty8NDQ0yO12q76+XlVVVaqpqVFtba0qKyvV0NAgl8ulpqYmSVJVVZV1GkSzpqYmuVyuVu8dFBQkf3//FuscDofCw8Ot583lQ0REhLy9vRUWFiY/Pz8FBwcrICBAgYGBCg0NldPpPOnREM0/AQC4QFAQAACAjs/j8cjHx0dvv/22Zs+ebXccAADao5VcghgAAAAAAIiCAAAAAAAAUBAAAAAAAAAKAgAAAAAAIAoCAAAAAAAgCgIAAAAAACAKAgAAAAAAIAoCAAAAAAAgCgIAAAAAACAKAgAAAAAAIAoCAAAAAAAgCgIAAAAAACAKAgAAAAAAIAoCAAAAAAAgCgIAAAAAACAKAgAAAAAAIAoCAAAAAAAgCgIAAAAAACAKAgAAAAAAIAoCAAAAAAAgCgIAAAAAACAKAgAAAAAAIAoCAAAAAAAgCgIAAAAAACAKAgAAAAAAIAoCAAAAAAAgCgIAAAAAACAKAgAAAAAAIAoCAAAAAAAgCgIAAAAAACAKAgAAAAAAIAoCAAAAAAAgyWGMMXaHAAAAOJtuuOEG7du3r8W6rKwsxcXFKSQkxFrn6+urd999V7Gxsec7IgAA7c1KH7sTAAAAnG09e/bUK6+80mr90aNHWzzv168f5QAAAP/BKQYAAKDDmT9/vhwOx7eO8fX11Y033nieEgEA0P5REAAAgA6ne/fu6t+//7eWBI2NjbruuuvOYyoAANo3CgIAANAhLVy4UN7e3id9zcvLSyNGjFBCQsJ5TgUAQPtFQQAAADqkefPmqamp6aSvORwOLVy48DwnAgCgfaMgAAAAHVKXLl00atQoeXmd/L87s2fPPs+JAABo3ygIAABAh7VgwYJW1yHw9vbWxIkTFRUVZVMqAADaJwoCAADQYc2ePbvVEQTGGC1YsMCmRAAAtF8UBAAAoMOKiIjQ5MmT5ePjY63z9fXVNddcY2MqAADaJwoCAADQoc2fP18ej0eS5OPjoxkzZigkJMTmVAAAtD8UBAAAoEObNm2aAgICJEkej0fz58+3OREAAO0TBQEAAOjQgoKCNHPmTElSaGioUlNTbU4EAED75PPdQwAAAOxTX1+vqqoqNTY2yu12yxij8vJy63WXy6WmpqZWv/f1cUlJSZKkIUOGaM2aNZKk4OBg+fn5nfQ9w8LC5O3t3WJcQECAAgMD5efnp+Dg4LM6RwAA2gOHMcbYHQIAAHQctbW1OnHihEpLS1VaWiqXyyW3263Kykq5XC65XC5VVlZa68rLy+V2u+V2u1VbW6uqqirV19erpqZGtbW1dk/nlLy9vRUWFiaHw6Hw8HDreXh4uEJCQhQSEqLQ0FA5nU6FhYUpNDTUWhceHq7w8HBFR0crMjJSoaGhdk8HAICVHEEAAAC+VW1trfLz85WXl6fCwkIdP37c2vlvXoqLi63HVVVVrbbh7++vkJAQa2e5eUc5JCREl1xyibVDHRQUpMDAQAUEBMjf319BQUHy8fFRaGiovLy85HQ6JUnh4eFyOByS/n0Kgb+//0mzh4aGWncwePDBB/XrX//aOjKgvLxcJ/uepKmpSS6Xy3rudrvV2Nio6upq1dXVqba2VjU1NWpoaFBlZaU8Ho8qKiqs52VlZVYBkpeXp4qKCqsUaV6+yc/PT1FRUa2W6OhoRUVFKSYmRnFxcYqNjVVcXJz1OQAAcDZxBAEAABex0tJSHT16VEePHtWxY8d0/PhxqwQoLCxUXl5ei8P5JSkmJkadOnVSZGRkqx3Zky1Op/OUh/KfT42NjS1ud2iX5lMfysrKVFJS0qpsaV6aXyspKVFhYaEaGhqsbQQGBqpLly7q0qWLVRrExsYqMTFR3bp1U9euXRUXFycvLy43BQA4bSspCAAA6MAqKyuVlZWlI0eOKDs72yoDmpfmb7MdDoe6dOmiuLg4a8fz6zufMTExio+PV0xMjHx9fW2e1cWpsLBQRUVFJy1x8vPzlZ+fr9zcXNXX10v691EJiYmJSk5OVteuXVssKSkp6ty5s80zAgC0MxQEAAB0BHl5eTp06JCOHDmiI0eO6LPPPtOhQ4d09OhR6wJ+ERERuuSSS1oszaVAr169uPBeB1FWVmb9O2hemkuEzMxMqxQKDw9X9+7drX8LvXv3Vp8+ffi3AAAXLwoCAAAuJKWlpdq7d6/27t2rffv2af/+/crIyFBdXZ0kqXPnzurVq5d69uyplJQUa+natWu7OMwf9jLG6Pjx48rKylJWVpYyMzOVkZGhrKwsZWdny+PxyMvLS8nJybr88svVv39/XXHFFerfv7+6detmXfcBANAhURAAANBeHTt2TB999JFVBuzbt0+5ubmSpNjYWGvHrXfv3lYpEB4ebnNqXKjq6up0+PBhZWZmKjMzU/v27dPevXv1xRdfqKmpSU6n0yoN+vfvr8GDB6tv377WRR8BABc8CgIAANqDxsZG7du3T+np6dq9e7fS09P11VdfydvbW8nJyerdu7cGDRqkQYMG6Qc/+IG6dOlid2RcJOrr63X48GHt3r3bWvbt26fKykoFBwfriiuu0KBBgzRq1CiNHTtW0dHRdkcGAJwZCgIAAOxQU1OjDz/8UGlpadq+fbv27Nmjuro6RUdHa/jw4dYyePBgBQUF2R0XaMHj8ejQoUPasWOHdu7cqV27dikzM1MOh0MpKSkaPny4JkyYoAkTJnAxRAC4cFAQAABwPhhjdODAAW3atElpaWn68MMPVVNTo969e2vMmDEaPny4hg0bph49etgdFTgjJSUl2rVrl3bu3KkPP/xQu3btUmNjo6644gpNmjRJkyZN0siRI+Xv7293VADAyVEQAABwrjQ2NmrLli16++23tW7dOhUUFKhTp06aMGGCJk6cqEmTJikhIcHumMA5UVlZqW3btlmlWGZmpoKCgjRu3Dhde+21mj59OtfMAID2hYIAAICzqbGxUVu3btVbb72lVatWqbS0VIMHD9bMmTM1adIkDRgwQF5eXnbHBM677OxspaWlae3atdq4caOMMZo4caLmzp2ra665hrIAAOxHQQAAwNlw+PBhrVy5Uq+88opKSko0aNAgzZ07V3PmzFG3bt3sjge0Ky6XS++8847efPNNpaWlSZKuuuoq3XbbbRo/fjy3UwQAe1AQAABwppqamrRhwwb96U9/0saNG9W1a1fdfPPNmjdvni655BK74wEXhPLycq1Zs0bPPfecPvjgA1122WX62c9+poULFyo0NNTueABwMaEgAACgrTwej55//nn97ne/05EjRzR+/HjdfvvtmjZtGqcPAN/Dvn37rCNxfHx8dMstt+jXv/61IiMj7Y4GABeDlfwvBgCANti4caMGDBig2267TRMmTNChQ4eUlpam6dOnd9hy4PXXX5fD4ZDD4VBAQIDdcS4YISEh1uf2Xcuzzz57yu089thj1riOfjZZNmkAACAASURBVFHL/v376+mnn1Zubq7+67/+Sy+//LIuvfRS/f73v1d9fb3d8QCgw+uY/5MBAOAsy8zMVGpqqiZPnqzu3bvr4MGDeuqpp9SrVy+7o51z8+bNkzFG48ePtzvKBaWyslKffvqpJGn69Okyxpx0GTNmTIvf6dGjh6ZOnWqtW7ZsmYwx6t+//3mfg10iIiK0bNkyffHFF/rpT3+q3/zmN+rdu7feeecdu6MBQIdGQQAAwHd49tlnNWjQIJWUlOj999/XqlWr1LNnT7tjoR0ICQnRqFGjztr2jDFqampSU1OTbRnak9DQUD300EPKzMzU8OHDNX36dN16662qqamxOxoAdEg+dgcAAKC9ampq0t13360nnnhCv/zlL/XAAw/I19fX7ljoYP75z39aj0NDQ/Xll1/aF6adSkxM1Msvv6xZs2bp5ptv1sGDB/XOO++oU6dOdkcDgA6FIwgAADiFO++8UytWrNArr7yiRx55hHIAZ9Xtt9+uJUuW2B3jgjJz5kzt2LFDBQUFmjhxotxut92RAKBDoSAAAOAknn/+ea1YsUJ/+9vf9MMf/tDuOJbVq1e3uLjd0aNHdd111yk8PFxRUVGaOnXqSb+BLi0t1dKlS9W9e3f5+fkpIiJCU6ZM0bZt21qNzcjI0IwZM+R0OhUcHKzRo0crPT39lJmKi4t1xx13qGvXrvLz81N0dLRmzZqlvXv3ntEcGxsb9cYbb2jixImKjY1VYGCg+vXrpyeffLLVofd1dXW677771KtXLwUFBSkyMlLTpk3TO++8I4/H0+Zxpzuf5gsHVlVVafv27dbfw8fnzA/O/Obftra29lvHn26G05nPN987MzNTc+fOVVRUlLWupKTkjOd2NqWkpGjr1q0qLCzUj370I7vjAEDHYgAAQAvl5eUmKirKLF261O4opzR9+nQjyUyfPt3s2LHDVFZWmrS0NBMYGGgGDx7cYmx+fr7p1q2biYmJMWvXrjUul8tkZmaaWbNmGYfDYZ555hlr7OHDh014eLiJj483mzZtMm632+zfv99MmjTJdO3a1fj7+7fYdl5enklOTjYxMTFm/fr1xu12m4MHD5oxY8aYgIAAs2PHjjbPbe3atUaSefjhh82JEydMcXGx+eMf/2i8vLzMsmXLWoy9+eabjdPpNJs2bTLV1dWmoKDALFu2zEgy27Zta/O4ts4nODjYjBw58pRz+fTTT42kUy6/+MUvWv1O89+2pqamxfr+/fub+Pj4VuO/LUNb59P83mPGjDHbtm0zVVVVZteuXcbb29sUFxefcp522Lx5s3E4HObdd9+1OwoAdBQrKAgAAPiGp59+2gQFBZny8nK7o5xS847c2rVrW6y/9tprjaQWO3OLFi0yksxrr73WYmxtba2Ji4szgYGBpqCgwBhjzJw5c4wk8/bbb7cYe/z4cePv79+qILjxxhuNJPPKK6+0WJ+fn2/8/f3NoEGD2jy3tWvXmrFjx7Zaf8MNNxhfX1/jcrmsdd26dTMjRoxoNbZnz54tdvxPd1xb53O6BcH06dNbvfazn/3snBcEbZ1P83tfKDvdqampZtq0aXbHAICOYgWnGAAA8A3bt2/XmDFj5HQ67Y7ynQYPHtzieWJioiQpLy/PWrdq1SpJ0tVXX91irL+/v8aPH6+amhpt3LhRkrRhwwZJUmpqaouxcXFxJ71zw+rVq+Xl5dXitnySFBsbqz59+mj37t3Kzc1t05ymTp160lMf+vfvr4aGBn322WfWusmTJ2vHjh269dZbtWvXLut0gczMTI0dO7bN487FfOx0pvMZMmTI+Yr4vUyfPl3bt2+3OwYAdBjcxQAAgG8oKytTZGSk3TFOyzdLDD8/P0myztWvq6uTy+VSQECAQkNDW/1+TEyMJKmgoEB1dXVyu90KCAhQSEhIq7GdO3dWVlaW9bx52yfL8XWHDx9WQkLCac/J5XLp8ccf16pVq5Sbm6vy8vIWr1dXV1uPV65cqeHDh+vFF1/U+PHjJUmjR4/W4sWLNXPmzDaNO1fzOZUVK1Z87218m+8zn+Dg4HOa7WyJioqSy+WSx+ORt7e33XEA4ILHEQQAAHxDUlKSMjMz7Y5xVvj7+8vpdKq2tvakV3wvLCyU9O9vlP39/RUaGqra2lpVVla2GnvixIlW2w4PD5ePj48aGhpkjDnpcuWVV7Yp87Rp0/Tggw/qlltuUVZWlpqammSM0RNPPCFJMsZYYx0OhxYsWKDNmzervLxcq1evljFGs2bN0u9///s2jTuT+TgcjjbN7Vw4VYZz9fdpTzIyMpSQkEA5AABnCQUBAADfcM0112j37t3av3+/3VHOiuZvyNevX99ifV1dnbZs2aLAwEDrlIIpU6ZI+r9TDZqVlJSctDSZNWuWGhsbT3qY9//8z/8oKSlJjY2Np53V4/Fo+/btio2N1R133KHo6GhrB7impqbV+PDwcGVkZEiSfH19NXHiROuK/F+f7+mOa+t8goKCVF9fbz1PSUnR008/fdrzPRu+LcPZ/vu0Jw0NDXr55Zc1ffp0u6MAQIdBQQAAwDeMHz9eQ4YM0U9+8hM1NDTYHed7e+SRR9StWzctWbJE69atk9vtVlZWlq6//nrl5+frySeftE41ePjhhxUZGaklS5YoLS1NlZWVOnTokG644YaTnnbwyCOPqHv37vrxj3+s9957Ty6XSydOnNBf/vIXPfDAA3rsscfadOs/b29vjR07VgUFBVq+fLlKSkpUU1Ojbdu26amnnjrp7/zkJz/R/v37VVdXp6KiIj366KMyxmjcuHFtHtfW+QwcOFBZWVnKycnRzp07deTIEY0ePfq053s2fFuGs/33aU8eeugh5eXlacmSJXZHAYCO4/xdEBEAgAvHgQMHTGhoqFm4cKHxeDx2x7Hs3Lmz1a3y7r33XmOMabX+6quvtn6vpKTELFmyxHTr1s34+voap9NpUlNTzZYtW1q9R2ZmppkxY4YJCwuzbpu4bt06M378eGvbN910kzW+tLTULF261FxyySXG19fXREdHm0mTJpm0tLQzmmNxcbFZvHixSUxMNL6+viYmJsYsWrTI/OpXv7Lev/nq+3v37jWLFy82l112mQkKCjKRkZFm2LBh5plnnjFNTU3WNk93XFvnk5GRYUaPHm2Cg4NNYmKiWblypfVacHBwq79JTEzMKee9atWqVuPnz59vli9ffsq/+XdlON35nOzfVXv+b+Krr75qvLy8Ws0VAPC9rHAY87UT+QAAgGXjxo2aMWOGrrrqKr388ssKCgqyOxJw0XvyySd111136c4779Ty5cvtjgMAHclKTjEAAOAUUlNTlZaWpvfff18/+MEP9Omnn9odCbhonThxQnPmzNHSpUv10EMPUQ4AwDlAQQAAwLcYNWqUPv30U8XExOgHP/iBFi5cqIKCArtjAReNhoYGPf3007rsssv04Ycfav369frlL39pdywA6JAoCAAA+A6JiYnaunWrXn/9dX3wwQe69NJLdf/995/0qvo4OYfD8Z3L/fffb3dMtDObN2/WwIEDdfvtt2vevHnKyMjQ5MmT7Y4FAB0W1yAAAKANqqur9fjjj+vRRx9VaGioFi9erFtvvVVdunSxOxrQIdTW1uqNN97QihUrtHv3bs2dO9e6EwcA4JxaSUEAAMAZyM/P1x//+Ef99a9/lcvl0uzZs3X77bdrxIgRdkcDLkjHjh3TU089pWeffVbl5eWaPXu27rzzTg0ZMsTuaABwsaAgAADg+/j6t52ffPKJ+vXrp+uuu05z585Vjx497I4HtGtlZWVavXq13nzzTaWlpalz584clQMA9qEgAADgbNm1a5deeukl/f3vf1dRUZEGDBigOXPmaO7cuerevbvd8YB2oby8XGvWrNGbb76pzZs3y8vLS6mpqbr++us1c+ZM+fr62h0RAC5WFAQAAJxtHo9H77//vt5880394x//UHFxsS6//HJNmjRJEydO1OjRoxUYGGh3TOC8MMZ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ALmAPPfSQwsLC9MADD5yX91u9erV1wUCHw6GjR4/quuuuU3h4uKKiojR16lR9+eWXrX6vtLRUS5cuVffu3eXn56eIiAhNmTJF27ZtazU2IyNDM2bMkNPpVHBwsEaPHq309PRTZiouLtYdd9yhrl27ys/PT9HR0Zo1a5b27t17RnOsq6vTfffdp169eikoKEiRkZGaNm2a3nnnHXk8njbP65ufWWZmpubOnauoqChr3a9+9Ss5HA5VVVVp+/bt1nofH58zmsOZmjJlihYsWKBf//rXampqOq/vDQBoBwwAALgg1dTUGKfTaZYvX37e33v69OlGkpk+fbrZsWOHqaysNGlpaSYwMNAMHjy4xdj8/HzTrVs3ExMTY9auXWtcLpfJzMw0s2bNMg6HwzzzzDPW2MOHD5vw8HATHx9vNm3aZNxut9m/f7+ZNGmS6dq1q/H392+x7by8PJOcnGxiYmLM+vXrjdvtNgcPHjRjxowxAQEBZseOHW2e280332ycTqfZtGmTqa6uNgUFBWbZsmVGktm2bdsZzevrn9mYMWPMtm3bTFVVldm1a5fx9vY2xcXFxhhjgoODzciRI9uc+WzKyMgwDofDbNy40dYcAIDzbgUFAQAAF6j333/fSDJHjx497+/dvLO7du3aFuuvvfZaI8na4TXGmEWLFhlJ5rXXXmsxtra21sTFxZnAwEBTUFBgjDFmzpw5RpJ5++23W4w9fvy48ff3b1UQ3HjjjUaSeeWVV1qsz8/PN/7+/mbQoEFtnlu3bt3MiBEjWq3v2bNni4KgLfMy5v8+s3ffffeU790eCgJjjLn88svNXXfdZXcMAMD5tYJTDAAAuEBlZGQoIiJCycnJtmUYPHhwi+eJiYmSpLy8PGvdqlWrJElXX311i7H+/v4aP368ampqtHHjRknShg0bJEmpqaktxsbFxalnz56t3n/16tXy8vLS1KlTW6yPjY3V/2fvzsOjqg/9j38myWSyLyQhYUkggISwiMiiQJAgEEDZEbACSrVKr20VrVZb7896tVeu1daLlZZiFbVSRa2A7EEKIiEoREBCTEJAIEB2kpB9/f7+8GZqTACDyAnh/Xqe82TmO2fOfM7M+Mj5zFn69OmjpKQknTx5skXrNH78eO3atUv33Xefdu/e7TysIC0tTbGxsRe1Xt80ZMiQFuWxwoABA5Sammp1DADAZUZBAADAFaqsrExeXl6WZvD39290393dXZKcx69XVVWpuLhYHh4e8vX1bfL80NBQSVJ2draqqqpUUlIiDw8P+fj4NJm3ffv2je43LLu+vl7+/v6NjvO32Wz6/PPPJUmHDx9u0TotWbJEb775po4eParRo0fLz89P48ePdxYCLV2vb/P29m5RHiv4+PiotLTU6hgAgMuMggAAgCtUUFCQCgoKVFtba3WUc3I4HPL391dlZaVKSkqaPJ6TkyPp61/8HQ6HfH19VVlZ2ezG6ZkzZ5osOyAgQG5ubqqpqZExptlp1KhRLcpss9k0b948ffTRRyoqKtLq1atljNH06dP1xz/+scXr1dLXbg2ys7MVEhJidQwAwGVGQQAAwBVq0KBBqqys1N69e62Ocl4Nl19cv359o/Gqqipt3bpVnp6ezkMKJkyYIOnfhxo0yM/PV1paWpNlT58+XbW1tUpISGjy2HPPPaeIiIgWFygBAQHO3evtdrvGjh3rvBLBN9ehJev1XXl5eam6utp5PyoqSsuWLWvRMr6vuro6JSQkaNCgQZf1dQEA1qMgAADgCtW7d29FR0dr+fLlVkc5r0WLFikyMlILFy7UunXrVFJSovT0dN1xxx3KysrS4sWLnbvkP/vss2rXrp0WLlyoLVu2qLS0VCkpKZo7d26zhx0sWrRI3bt31913362NGzequLhYZ86c0V//+lc9/fTTeuGFFy7qUoE//elP9cUXX6iqqkq5n9fXBAAAIABJREFUubn6/e9/L2OMbr755otar+/q+uuvV3p6ujIzM5WYmKijR49qxIgRLc7/fWzcuFE5OTnOAgQAcBWx7gSJAADg+1q2bJlxd3c3aWlpl+X1EhMTjaRG0xNPPGGMMU3Gb731Vufz8vPzzcKFC01kZKSx2+3G39/fjBs3zmzdurXJa6SlpZmpU6caPz8/52UT161bZ0aPHu1c9j333OOcv6CgwDz88MOmW7duxm63m5CQEBMXF2e2bNlyUeu4f/9+s2DBAhMdHW28vLxMu3btzI033mheeeUVU19f32je77Jezb1n5/onWGpqqhkxYoTx9vY24eHhZsmSJRe1DherpqbGXHfddWbixImX9XUBAK3CyzZjjLGglwAAAJdAXV2dBg8eLIfDoR07dshut1sdCVewX/7yl1q6dKmSkpLUq1cvq+MAAC6vJRxiAADAFczV1VUrVqzQwYMHtWDBAtH742L9/e9/14svvqhXX32VcgAArlItPygPAAC0KtHR0Xrvvfc0ZcoUGWO0bNky9iRAi7z55pu699579dhjj+n222+3Og4AwCLsQQAAQBswYcIEbdiwQatWrdL48eNVVFRkdaRWxWazXXB66qmnrI5picWLF2v+/Pn66U9/qv/+7/+2Og4AwEKcgwAAgDbkwIEDuvXWW+Xj46PXXntNw4YNszoSWqns7Gzdf//9Wrt2rZYsWaL77rvP6kgAAGtxDgIAANqS/v3767PPPlP37t01YsQIPfTQQyovL7c6FlqZt956S3369NH+/fu1ZcsWygEAgCQOMQAAoM3p2LGj1q9fr+XLl+uNN95Q37599dZbb6m+vt7qaLDY3r17NXbsWN11112aM2eODh48qNjYWKtjAQBaCQoCAADaqDvvvFOHDh1SbGys5s+fr/79+2vNmjVc6eAqlJKSohkzZmjIkCEqKyvTJ598opdeekne3t5WRwMAtCIUBAAAtGEdOnTQa6+9puTkZEVHR2vatGkaNGiQXn/9dVVWVlodDz+wjz/+WDNnztS1116rjIwMrVmzRrt27eLcFACAZlEQAABwFejVq5feffddJSUlqVevXlqwYIEiIiL0m9/8RpmZmVbHwyVUXl6uV155Rf3791dsbKxOnTqlf/zjH9q3b58mTZpkdTwAQCvGVQwAALgK5eTk6PXXX9eSJUt06tQpDR06VDNnztScOXMUHBxsdTy0UF1dnRITE/X3v/9d77zzjiorKzVlyhQtXLiQvQUAAN/VEgoCAACuYtXV1Vq3bp1WrFihDRs2yBijCRMmaPbs2Ro/frwCAgKsjohzqK2t1c6dO/X+++/rvffeU25urm688Ub96Ec/0o9+9COFhIRYHREAcGWhIAAAAF8rLi7WqlWr9Pbbb2vr1q2y2WwaNmyYJkyYoFtuuUXXXnut1RGvetnZ2dqwYYM2btyoLVu2qLi4WL1793aWAt27d7c6IgDgykVBAAAAmiosLFR8fLw2bNigTZs2KTc3V507d9aoUaMUExOjESNGqFevXrLZbFZHbdNycnK0c+dOffLJJ/r444914MABORwOxcbGOoubHj16WB0TANA2UBAAAIDzq6+vV1JSkjZt2qQdO3Zo9+7dKi0tVUhIiIYPH64RI0bo+uuv14ABA+Tv72913CtWTU2NkpOTtW/fPiUkJGjnzp1KT0+Xq6ur+vfvr5iYGMXFxWnUqFHy8vKyOi4AoO2hIAAAAC1TW1urzz//XDt37tSOHTuUmJio3Nxc2Ww2devWTQMGDHAWBtHR0YqIiGBPg285c+aMvvzyS+3fv1/79u3Tvn37lJycrOrqanl5eWngwIG66aabFBMTo2HDhsnPz8/qyACAto+CAAAAfH8nT550bug2TMePH5ckeXl5KSoqSj179lSvXr0UFRWla665RhEREWrfvr3FyX84paWlOnHihI4cOaLU1FSlp6crNTVVqampys/PlyQFBARowIABjaZevXrJ1dXV4vQAgKsQBQEAAPhhnDlzxrlBnJaWpvT0dH355Zc6evSoampqJEkeHh6KiIhQeHi4wsPD1aVLF3Xq1Ent27dXcHCwgoOD1b59ewUGBlq8Nv9WXl6u/Px8ZWdnKz8/X/n5+Tp9+rQyMzN14sQJnThxQpmZmSosLHQ+p3PnzoqKilJUVJSzJImKilKXLl0sXBMAABqhIAAAAJdXTU2Njh8/rszMTGVmZurYsWPO25mZmTp58qTOnj3b6Dl2u91ZGPj6+srLy0uBgYHy9vaWl5eXfH195efnJ1dXV7m7u8vb29v5XDc3N/n6+jrvV1ZWqqKiwnm/urpaZWVlkqSzZ8+qrKxM5eXlKioqUmlpqcrLy1VaWqq8vDzl5+ervLy8UTYPDw+FhYU5C46GsiMiIkJdunRR165d5ePj80O8lQAAXEoUBAAAoPWpqqpy/jqfk5Pj3DjPz89XaWmpysrKVFRUpLKyMpWVlam0tFRFRUUyxqiiokKVlZWNlvXNjXq73d5og93V1dV5jL+vr6+8vb3l7e2tgIAA520fHx9nQREcHKyQkBC1b99eISEhbPwDANoKCgIAAND21dXVyc3NTe+//75mzJhhdRwAAFqjJS5WJwAAAAAAANajIAAAAAAAABQEAAAAAACAggAAAAAAAIiCAAAAAAAAiIIAAAAAAACIggAAAAAAAIiCAAAAAAAAiIIAAAAAAACIggAAAAAAAIiCAAAAAAAAiIIAAAAAAACIggAAAAAAAIiCAAAAAAAAiIIAAAAAAACIggAAAAAAAIiCAAAAAAAAiIIAAAAAAACIggAAAAAAAIiCAAAAAAAAiIIAAAAAAACIggAAAAAAAIiCAAAAAAAAiIIAAAAAAACIggAAAAAAAIiCAAAAAAAAiIIAAAAAAACIggAAAAAAAIiCAAAAAAAAiIIAAAAAAACIggAAAAAAAIiCAAAAAAAASLIZY4zVIQAAAC6luXPn6sCBA43G0tPT1bFjR/n4+DjH7Ha7NmzYoLCwsMsdEQCA1maJm9UJAAAALrWePXtqxYoVTcaPHTvW6H6/fv0oBwAA+D8cYgAAANqcOXPmyGaznXceu92uu+666zIlAgCg9aMgAAAAbU737t3Vv3//85YEtbW1mj179mVMBQBA60ZBAAAA2qQ777xTrq6uzT7m4uKiYcOGqXPnzpc5FQAArRcFAQAAaJNuv/121dfXN/uYzWbTnXfeeZkTAQDQulEQAACANqlDhw6KiYmRi0vz/9yZMWPGZU4EAEDrRkEAAADarHnz5jU5D4Grq6vGjh2roKAgi1IBANA6URAAAIA2a8aMGU32IDDGaN68eRYlAgCg9aIgAAAAbVZgYKDGjx8vNzc355jdbtfkyZMtTAUAQOtEQQAAANq0OXPmqK6uTpLk5uamqVOnysfHx+JUAAC0PhQEAACgTZs0aZI8PDwkSXV1dZozZ47FiQAAaJ0oCAAAQJvm5eWladOmSZJ8fX01btw4ixMBANA6uV14FgAAAOvU19eruLhYklRYWChJKi4uVn19vSSppKREtbW1zT63Yf6IiAhJ0pAhQ7RmzRpJko+Pj+x2e7PPCwgIcF79wNfXV25ubvL29pa7u7s8PT2deyQAANCW2IwxxuoQAACgbTlz5ozy8/NVUFCg4uJilZSUqLCwUCUlJSotLVVJSYlz7Jv3S0pKVFVVpfLyctXU1Ki0tNTqVTknh8MhLy8v2e12+fj4yMPDQ76+vvL19VVgYKB8fHyc9319fRUQEOC87e/vr6CgIOd0rqICAIDLaAkFAQAAuKAzZ84oKyvLOeXk5Cg3N1cFBQXNTg0nBfymb240+/j4yM/PTwEBAY3G/P395ebmJl9fX7m4uMjf39/5XEny9/eXi4uL81d9SfLw8JCnp2ezub+5l8Azzzyj3/zmN3J1dZUkFRUVqbl/BtXW1qqkpMR5v2FvhYY9FcrKylRdXa3y8nJVVVWpsrJSFRUVKi8vd5YdRUVFzsKjYayhDKmpqWnymv7+/goODlZwcHCj4iAoKEhhYWEKCwtTx44d1aFDB7Vv3965DgAAXEIUBAAAXM3q6+t1+vRpHTt2TF999ZVOnDihnJwcnTp1StnZ2Tp9+rSys7NVWVnpfI7D4VBoaKhCQkKcG7Tf3rD95sZuwy/nVqutrW10uUOrVFZWqqioqEmpkpeXp4KCAueeFw1TVlZWo8LC1dVV7du3V4cOHdSxY0dnedCpUyd17dpVXbt2VZcuXeRwOCxcSwDAFYiCAACAtu7MmTNKT0/X0aNHdezYsUbT8ePHVV1dLUlyd3dX586dnb9UN0wN9xs2RoOCgixeo6tPeXl5k9Lmm/ezsrKUmZnpLBJsNps6dOigyMhIRUZGOouDrl276pprrlF4eLjzHAsAAPwfCgIAANqC2tpanThxQkePHtXRo0d16NAhpaSkOO9Lkt1uV3BwsDp27Khu3bo1mbp06cKu61e4wsJCZ2HQ8Nk3TBkZGc6TPbq7u6tHjx7q06eP8/Pv3bu3+vfv3yr29gAAWIKCAACAK01+fr727dun/fv3a//+/Tpw4IAOHz7s3BOgU6dOioqKUlRUlHr16uW8HR4eTgFwlcvLy1N6erpSU1OVlpamtLQ0paam6quvvlJNTY1sNpsiIiLUr18/XXfddc6pW7du7HEAAG0fBQEAAK3ZqVOntHv37kaFwKlTpyRJHTt21HXXXaf+/furb9++ioqKUs+ePfkFGC1WU1Ojo0eP6ssvv1RaWprzu3b48GHV1dXJz89P/fv3dxYGgwcPVp8+feTi4mJ1dADApUNBAABAa1FXV6fU1FQlJCRo586dSkpKUkpKiiSpQ4cOGjhwoHMaPHiwwsLCLE6Mtq66ulqHDx9WUlKSc9q/f7/Kysrk4+Oj/v37KyYmRsOHD9fw4cPVrl07qyMDAC4eBQEAAFapra3V7t27FR8frx07dmjPnj0qLy9XYGCghg4dqqFDh2rYsGEaMmSIfHx8rI4LSPq6yDp48KASEhKUmJioXbt26auvvpKrq6v69OmjmJgYjRkzRqNHj5afn5/VcQEA3x0FAQAAl9OxY8e0efNmxcfHa+vWrSouLlbXrl01atQoDR8+XEOHDlV0dDTHe+OKkpWVpcTERCUkJOiTTz5RUlKSXFxcNHToUMXFxWncuHEaOHAghyQAQOtGQQAAwA8tKSlJ7777rlavXq309HR5e3srNjZW48aN07hx49SzZ0+rIwKXVH5+vj766CPFx8dr8+bNOn36tIKDgzVhwgTNmjVLcXFxcnd3tzomAKAxCgIAAH4IX3zxhVauXKl3331XGRkZioyM1MyZMzV+/HgNHz6cjSNcVQ4ePKj4+HitWrVKu3btkr+/v6ZNm6ZZs2Zp9OjRstvtVkcEAFAQAABw6RQXF2v58uVatmyZvvzyS4WHh2vWrFmaPXu2Bg8ebHU8oFXIzMzUe++9p5UrV+qzzz5TUFCQ5syZo5///Oe65pprrI4HAFczCgIAAL6vtLQ0/elPf9Ibb7whSZo3b57mzp2roUOHci4B4Dy++uorvfPOO1q2bJlOnDih8ePH64EHHlBcXBz/7QDA5beEM8UAAHCRPv/8c91yyy2Kjo7Wpk2b9PTTT+vkyZP685//rGHDhrGBA1xAZGSkfv3rXysjI0P//Oc/VVlZqfHjxys6OlpvvfWW+B0LAC4vCgIAAFro1KlTmj9/vgYPHqzi4mJ9+OGHSk9P10MPPSR/f3+r4/0g3nnnHdlsNtlsNnl4eFgd54Jamvd88/v4+Dgf+/bk4eGha6+9VkuWLGFj9ntwdXXV1KlTtXXrVh08eFDDhw/X/PnzdcMNN2jnzp1WxwOAqwYFAQAA31FNTY3+67/+S1FRUdqxY4feeecd7dy5UxMnTmzzl2+7/fbbZYzR6NGjrY7ynbQ07/nmLy0t1b59+yRJU6ZMkTFGxhhVVVVp9+7d8vPz089//nM99thjl3QdrlZ9+/bVq6++qn379ikwMFAjRozQzJkzderUKaujAUCb17b/NQMAwCVy5MgRDR8+XC+88IJ++9vf6ssvv9TMmTM5jOAq5u7uruuuu05vv/22XFxc9OKLL+rMmTMXvTwfHx/FxMRcwoRXtn79+mnz5s1av369vvjiC/Xv319r1661OhYAtGkUBAAAXMCePXs0dOhQ1dbWKikpSY8++qgcDofVsdBKhIeHq0OHDqqtrdWBAwesjtPm3HLLLfr88881ZcoUTZ06VYsXL7Y6EgC0WW5WBwAAoDU7ePCgxo4dq+HDh+vdd9+Vt7e31ZHQCjWcf+BKOD/Dlcjb21uvvvqqoqOj9dBDD8nNzU0/+9nPrI4FAG0OexAAAHAOpaWlmjx5sgYMGKBVq1a1mnJg9erVjU6Ud+zYMc2ePVsBAQEKCgrSxIkTdeTIkSbPKygo0MMPP6zu3bvL3d1dgYGBmjBhgrZt29Zk3tTUVE2dOlX+/v7y9vbWiBEjznuyuLy8PD3wwAPq2rWr3N3dFRISounTp2v//v0XtY61tbVauXKlxo4dq7CwMHl6eqpfv35avHix6uvrv3fels5/PidOnFBWVpb8/PzUp0+fRo99l/flhRdekM1mU1lZmRISEpyfq5vb17/j/O53v3OOffMQhE2bNjnHg4ODnePf/n6kpaVp1qxZCgoKco797W9/u6jvkNUeeeQRPfvss3rwwQeVkJBgdRwAaHsMAABo1m9+8xvTrl07k5OTY3WUZk2ZMsVIMlOmTDG7du0ypaWlZsuWLcbT09MMHjy40bxZWVkmMjLShIaGmrVr15ri4mKTlpZmpk+fbmw2m3nllVec8x4+fNgEBASYTp06mfj4eFNSUmK++OILExcXZ7p27WocDkejZZ8+fdp06dLFhIaGmvXr15uSkhKTnJxsRo4caTw8PMyuXbtavG5r1641ksyzzz5rzpw5Y/Ly8sxLL71kXFxczCOPPNJo3pbmben8xhizb98+53vdoLq62uzbt88MHz7cuLu7mzfffPN7vS/e3t5m+PDh53xPzvX4wIEDTVBQUJPxhu/HyJEjzbZt20xZWZnZvXu3cXV1NXl5eY3m+S7fodZkwoQJpl+/fqa+vt7qKADQlrxMQQAAQDNqampM+/btzTPPPGN1lHNq2Lhbu3Zto/HbbrvNSHJuBBpjzPz5840k8/bbbzeat7Ky0nTs2NF4enqa7OxsY4wxM2fONJLM+++/32jeU6dOGYfD0WQD+q677jKSzIoVKxqNZ2VlGYfDYQYOHNjidVu7dq2JjY1tMj537lxjt9tNcXGxc6yleVs6vzH/Lgiam6ZNm2YyMjKaPKel78sPVRBs2LDhnMtsyXeoNTlw4ICRZD755BOrowBAW/IyhxgAANCMjIwM5ebmatKkSVZHuaDBgwc3uh8eHi5JOn36tHNs1apVkqRbb7210bwOh0OjR49WRUWFNm/eLOnrXdclady4cY3m7dixo3r27Nnk9VevXi0XFxdNnDix0XhYWJj69OmjpKQknTx5skXrNHHixGYPfejfv79qamp06NAh51hL87Z0/m/65mUOT548qdmzZ2vVqlVatmxZk3l/iPflYgwZMuSC83yX71Brcu2116pLly4cZgAAlxgFAQAAzSgsLJQkBQUFWZzkwvz9/Rvdd3d3lyTnsfpVVVUqLi6Wh4eHfH19mzw/NDRUkpSdna2qqiqVlJTIw8NDPj4+TeZt3759o/sNy66vr5e/v3+j49ptNps+//xzSdLhw4dbtE7FxcV68skn1a9fPwUGBjqX9+ijj0qSysvLna/f0rwtmf98OnXqpNdff13du3fX888/r7179zZ6nR/ifbkY3+XcGRf6DrVGwcHB3+uykgCApigIAABoRkREhCQpLS3N4iTfn8PhkL+/vyorK1VSUtLk8ZycHElf/7LtcDjk6+uryspKlZaWNpn32xtkDodDAQEBcnNzU01NjfPX9W9Po0aNalHmSZMm6ZlnntG9996r9PR01dfXyxijF198UdK/rxpwMXlbMv+FeHh46Nlnn5UxRo8//nij12np+2Kz2c77Wi4uLqqurm4yXlRU1KLMbUFNTY2OHDmirl27Wh0FANoUCgIAAJrRqVMnXX/99Vq+fLnVUS6JadOmSZLWr1/faLyqqkpbt26Vp6enc5f7CRMmSPr3rvgN8vPzmy1Mpk+frtra2mZ3937uuecUERGh2tra75y1rq5OCQkJCgsL0wMPPKCQkBDnxnNFRUWT+Vuat6XzX8jMmTM1YMAAbd26VVu2bHGOt/R98fLyalQAREVFNTp0oUOHDjp16lSj5WRnZ+vEiRMtznyl++CDD3T27Nkmh28AAL4fCgIAAM7h17/+tf7xj380eyz8lWbRokWKjIzUwoULtW7dOpWUlCg9PV133HGHsrKytHjxYuehBs8++6zatWunhQsXasuWLSotLVVKSormzp3b7G75ixYtUvfu3XX33Xdr48aNKi4u1pkzZ/TXv/5VTz/9tF544QXnJfu+C1dXV8XGxio7O1vPP/+88vPzVVFRoW3btmnp0qVN5m9p3pbOfyE2m02/+93vJEmPP/64c++Glr4v119/vdLT05WZmanExEQdPXpUI0aMcD4eFxen06dP6+WXX1ZpaamOHDmiBx98sEWHRbQFhYWF+tWvfqW5c+c6z5UAALhELvdpEQEAuJLMmjXLBAUFmZSUFKujOCUmJjY5k/4TTzxhjDFNxm+99Vbn8/Lz883ChQtNZGSksdvtxt/f34wbN85s3bq1yWukpaWZqVOnGj8/P+cl79atW2dGjx7tXPY999zjnL+goMA8/PDDplu3bsZut5uQkBATFxdntmzZclHrmJeXZxYsWGDCw8ON3W43oaGhZv78+ebxxx93vv43rwLQ0rwtmd/b27vJ+zp79uwmmWNiYpyPN1xtoCXvS2pqqhkxYoTx9vY24eHhZsmSJY0eLyoqMj/5yU9Mhw4djKenp4mJiTF79uwxAwcOdL7uY4891uz349v/5LvY75DVysvLzahRo0xERITJzc21Og4AtDUv24z5v5obAAA0UVFRobi4OKWmpurDDz/U0KFDrY4EXJXy8/M1depUffnll9q+fbv69etndSQAaGuWcIgBAADn4enpqc2bN2vo0KGKjY3VCy+80KrP7A60Rdu3b9eAAQN06tQp7dy5k3IAAH4gFAQAAFyAl5eX1qxZo9///vd64oknNHjwYH388cdWxwLavFOnTmnBggUaPXq0Bg0apKSkJEVHR1sdCwDaLAoCAAC+A5vNpgcffFBffPGFunfvrtjYWE2aNEkZGRlWR7ti2Gy2C05PPfWU1THRCpSXl+u5555Tr169tGnTJi1fvlwffPCB2rVrZ3U0AGjTOAcBAAAXYdOmTfrlL3+pjIwMzZo1S7/4xS80ZMgQq2MBV7RTp05p6dKlWrZsmaqqqvTEE0/ogQcekMPhsDoaAFwNOAcBAAAXY/z48Tpw4ICWLVumQ4cO6YYbbtANN9ygFStWNLqWPYALS0hI0O23367IyEgtW7ZM9913nw4fPqxHH32UcgAALiP2IAAA4BLYuXOnXnrpJa1atUrt2rXTrFmzNHv2bA0bNkwuLvTxwLcdOXJE7777rt5++20dPHhQgwYN0i9+8QvNnj2bUgAArLGEggAAgEvo5MmTeu2117Ry5UqlpKSoc+fOmjlzpmbPnq0hQ4bIZrNZHRGwzIkTJ/Tee+9p5cqV2rNnj4KDgzVjxgzNnz9fN954o9XxAOBqR0EAAMAPJTk5WStXrtTKlSt1+PBhdenSRePGjVNcXJxGjx6tgIAAqyMCP6iamholJiZq8+bNio+PV1JSkgICAjRt2jTNnj1bN998s9zc3KyOCQD4GgUBAACXw759+7R69WrFx8drz549kqQhQ4Y4C4MhQ4bI1dXV4pTA95eRkaH4+HjFx8frX//6l0pKStS9e3fFxcVp4sSJGjNmjNzd3a2OCQBoioIAAIDLrbS0VNu2bdO6deu0efNmHT9+XN7e3rruuusUExOj4cOHa9iwYQoKCrI6KnBetbW1SktLU0JCgnbu3KlPPvlEx44dk5eXl4YNG6YxY8ZozJgxGjhwoNVRAQAXRkEAAIDVUlJStGPHDiUmJmrXrl3KyMiQi4uLevfurWHDhmnYsGEaMGCAoqOjZbfbrY6Lq9ixY8e0f/9+ffrpp0pISNDevXtVUVGhoKAgDR06VMOGDVNMTIxuvPFGvqsAcOWhIAAAoLXJzc1VYmKiEhIStGvXLiUlJamyslLu7u7q27evrrvuOvXv39/519/f3+rIaGOqq6uVkpKi/fv368CBA9q/f7/279+voqIiubi4qFevXho6dKiGDx+uoUOHKioqihNwAsCVj4IAAIDWrra2Vqmpqc6NtIapoKBAktStWzdFR0crOjpaPXv2VFRUlKKjoxUSEmJxcrR2ZWVlSktLU3p6ur788kulpaUpNTVVKSkpqqmpkYeHh/r27asBAwY4C6lrr71Wvr6+VkcHAFx6FAQAAFypMjMzdeDAAR04cEApKSnODb2SkhJJUmBgoLMsiIqKUmRkpLp27aquXbuqffv2FqfH5VJaWqpjx445p9TUVKWnpystLU0nTpyQJNntdmfRFBUVpWuvvVb9+/dXVFQUVxkAgKsHBQEAAG3NyZMnlZaW5vw1uOH2yZMnVVdXJ0ny9PRsVBh07dpVXbp0UUREhDp37qzQ0FCOIb9C5OTkKCcnRydOnGhUBBw7dkzHjx9Xfn6+c96QkBBdc8016tWrl6KiohQVFaVevXqpW7dufN4AAAoCAACuFjU1NcrMzGy08fjVV185758+fdpZIEhSaGioQkNDnYXBt/8GBQUpJCREAQEBFq5V21ReXq6CggLl5+crKytLOTk5OnnyZLN/a2pqnM8LDg5uVPg03G4og7y9vS1cKwBAK0dBAAAAvlZTU6PTEvrhAAAgAElEQVRTp07p9OnTysrKavQ3Oztbp06dUnZ2dqNfpCXJ1dVVQUFBTabg4GAFBwfL399fgYGB8vHxka+vr3MKCAiQr69vm/3lurCwUCUlJSotLVVJSYlKSkpUWFio0tJSFRcXq6CgwDnl5eU1ul9ZWdloWV5eXucsajp16qSwsDCFh4fLx8fHorUFALQBFAQAAKBlqqqqlJOT0+yG7benvLw8nT17VoWFhedcnsPhkK+vr/z8/BQQECCbzeb86+fnJ1dXV/n4+Mhut8vLy0sOh0MeHh7y9PR0LqNh/Nvc3NyaPaFeVVWVysvLm4zX19eruLjYeb+2tlYlJSWNxhvWpbi4WPX19SopKXHO981C4Fx8fHzk5+fXqEwJCQlptmQJCgpSWFgYJwUEAFwOFAQAAODyaO7X9IbbDY8VFhbKGKOioiJJUlFRkYwxOnv2rOrq6lRaWqqamhqVl5erqqrKueyGjfVvq6ioaPJrvPT1Xg9+fn7N5mwoJSTJxcXFeRnJwMBASZK/v79cXFzk6+srNzc3eXt7y93dXd7e3s69IxrKjm/uMdGw1wSXAwQAtFIUBAAAoO2rq6uTm5ub3n//fc2YMcPqOAAAtEZLXKxOAAAAAAAArEdBAAAAAAAAKAgAAAAAAAAFAQAAAAAAEAUBAAAAAAAQBQEAAAAAABAFAQAAAAAAEAUBAAAAAAAQBQEAAAAAABAFAQAAAAAAEAUBAAAAAAAQBQEAAAAAABAFAQAAAAAAEAUBAAAAAAAQBQEAAAAAABAFAQAAAAAAEAUBAAAAAAAQBQEAAAAAABAFAQAAAAAAEAUBAAAAAAAQBQEAAAAAABAFAQAAAAAAEAUBAAAAAAAQBQEAAAAAABAFAQAAAAAAEAUBAAAAAAAQBQEAAAAAABAFAQAAAAAAEAUBAAAAAAAQBQEAAAAAABAFAQAAAAAAEAUBAAAAAACQZDPGGKtDAAAAXEpz587VgQMHGo2lp6erY8eO8vHxcY7Z7XZt2LBBYWFhlzsiAACtzRI3qxMAAABcaj179tSKFSuajB87dqzR/X79+lEOAADwfzjEAAAAtDlz5syRzWY77zx2u1133XXXZUoEAEDrR0EAAADanO7du6t///7nLQlqa2s1e/bsy5gKAIDWjYIAAAC0SXfeeadcXV2bfczFxUXDhg1T586dL3MqAABaLwoCAADQJt1+++2qr69v9jGbzaY777zzMicCAKB1oyAAAABtUocOHRQTEyMXl+b/uTNjxozLnAgAgNaNggAAALRZ8+bNa3IeAldXV40dO1ZBQUEWpQIAoHWiIAAAAG3WjBkzmuxBYIzRvHnzLEoEAEDrRUEAAADarMDAQI0fP15ubm7OMbvdrsmTJ1uYCgCA1omCAAAAtGlz5sxRXV2dJMnNzU1Tp06Vj4+PxakAAGh9KAgAAECbNmnSJHl4eEiS6urqNGfOHIsTAQDQOlEQAACANs3Ly0vTpk2TJPn6+mrcuHEWJwIAoHVyu/AsAAAA1igsLJQklZWVqbq6WtXV1SorK3M+XlxcrPr6+maf+815IyIiJElDhgzRmjVrJH19LoLzHWrg5+cnV1dXSZK7u7u8vb0lfX1eA0ny8fGR3W7/PqsHAECrYjPGGKtDAACAK1tRUZEKCwt15swZFRYW6uzZsyovL1dZWZmKiopUVlamsrIylZaWqri4WGVlZSovL1dxcbFKSkpUW1ur0tJS1dTUNCkBWrvmyoOAgAB5e3vLy8tLfn5+8vPzk5eXl7y9vZ2PeXt7y8fHR/7+/vLz81NgYKDatWunwMDARidVBADgMlnC/30AAICTMUa5ubnKy8tTTk6OsrOzlZeXp4KCgkYFwLf/NvcrvsPhaLRB7OXlJV9fX/n5+SkgIECdOnVyPubu7i4vLy85HA65ubnJ19dXkuTv7y8XFxd5enrKw8Oj0WOSnOPNsdlsCggIcN5/5pln9Jvf/Ma5V0B5ebmqqqqafW5dXZ3Onj3rvF9ZWamKiopG42fPnlVdXZ3zsfr6ehUXF8sY06QUyczMVHl5ucrLy5s81pxvFwbf/BsUFKSQkBCFhIQoLCxMoaGhCgkJkcPhON9HCwDABbEHAQAAV4GamhqdPn1amZmZOnHihE6fPq2srCzl5uYqNzdXWVlZysvLU15envOM/9LXu+GHhIQoKCio2Y3Vhr/fHvvm7vmtRW1tbav8Zb64uFhnz549ZwHz7dtnzpxRbm6uysvLGy0nMDBQoaGhat++vUJDQxUWFqaQkBB17tzZOXXp0kVeXl4WrSkAoJVbQkEAAEAbUFhYqCNHjujEiRPKzMzU8ePHdfLkSZ08eVLHjx9Xdna281d+u92usLAwdezYUSEhIWrfvr06dOig9u3bO283jAcHB1u8ZjiXsrIyZWdnKycnR3l5ecrKymp0u2FPkIa9Fxq0a9dO4eHhCg8PV0REhDp37qzw8HB16dJF3bp1U6dOnSxcKwCAhSgIAAC4UhQWFuro0aPnnBoEBgaqW7du6tChgzp27NjkdkRERKv8JR0/nIqKCmVlZTm/Kw17kDTcPn78uPO8D+7u7urcubO6devWaOrdu7eioqL47gBA20VBAABAa3Ps2DGlpKQoOTlZycnJOnTokNLT053HqzscDnXr1k09evRwTt27d1ePHj0UHh4ud3d3i9cAVxpjjLKzs5WRkaEjR440+puRkaGioiJJX+99EhkZqT59+qh3797q16+f+vTpo6ioKK7oAABXPgoCAACsUlRUpKSkJB08eFCHDh3SwYMHlZKSopKSEklS586dnRthUVFRziKgc+fOcnFxsTg9riYFBQXO0iA9PV2HDh1ScnKyMjIyVFtbK7vdrp49e6pPnz7q27ev+vTpo4EDB6pLly5WRwcAfHcUBAAAXA6lpaXav3+/kpKSnFNqaqrq6+sVGBio3r17O3+V7dOnj6699lq1b9/e6tjAedXU1CgzM1OHDh1SUlKSUlJSdOjQIaWlpamurk7+/v7q27evBg4cqJiYGMXExKhDhw5WxwYANI+CAACAH8LRo0e1fft27dixQ3v37lVqaqrq6uoUEhKiQYMGafDgwRo0aJAGDRrEBhPanNLSUu3bt0979+7Vnj17tHfvXmVkZMgYo86dO2vQoEEaPny4YmNjNWDAgFZ3xQsAuEpREAAAcCl89dVX2r59u3M6ceKEPD09dcMNN+jGG290lgHsco2rVcMhNXv27NGePXu0c+dO5ebmys/PTyNGjFBsbKxGjhyp66+/nsIAAKxBQQAAwMWoqKjQ1q1btWbNGsXHxzsLgaFDh2rkyJEaNWqUhgwZIofDYXVUoFUyxiglJcVZqu3YscNZGMTGxmrSpEmaNGmSQkNDrY4KAFcLCgIAAL6rvLw8rVu3TmvXrtXmzZtVWVmpwYMH65ZbbqEQAL6nhsJg27Ztio+P10cffaSqqirdcMMNmjx5sqZMmaLo6GirYwJAW0ZBAADA+ZSVlendd9/V66+/roSEBNntdo0ePVqTJ0/WpEmTOH8A8AMpLy/Xli1b9OGHH2rdunXKzc3VNddcozlz5ujHP/6xIiIirI4IAG0NBQEAAM359NNP9eqrr+qdd95RdXW1pk2bppkzZ2rcuHHy9va2Oh5wVamvr9fu3bv1wQcf6K233lJeXp7i4uJ09913a8qUKXJ3d7c6IgC0BRQEAAA0qKqq0htvvKE//elPSk5OVr9+/XTPPfdo3rx5ateundXxAOjrSyuuW7dOr732mjZu3KjAwEDdfffdWrhwIXv0AMD3s8TF6gQAAFitqqpK//u//6vIyEg98MADuuGGG/Tpp5/qiy++0IMPPtjmy4F33nlHNptNNptNHh4eVscBzstut2vatGlau3atjh8/roULF+rvf/+7IiMj9R//8R86ffq01REB4IrFHgQAgKvaP//5T/3qV79SVlaWfvazn+nhhx++an+FHDNmjHbu3KnKykqrowAtUlVVpddff12LFi1Sfn6+HnnkET3++OMUXgDQMuxBAAC4OuXm5mrGjBmaOXOmYmJilJ6erueff/6qLQfQcj4+PoqJiWmzr3clcTgcWrBggVJTU/Xkk0/qxRdf1IABA5SYmGh1NAC4olAQAACuOp9//rkGDRqkffv26aOPPtIbb7yhzp07Wx0LwPfk4eGhX/3qV0pOTlaXLl00cuRILV261OpYAHDFoCAAAFxVdu/erdjYWPXq1UtJSUm6+eabrY4E4BILDw/Xxo0b9cwzz+hnP/uZnn32WasjAcAVgYIAAHDVOH78uCZMmKDRo0dr/fr1CgwMtDpSE6tXr3aeMNBms+nYsWOaPXu2AgICFBQUpIkTJ+rIkSNNnldQUKCHH35Y3bt3l7u7uwIDAzVhwgRt27atybypqamaOnWq/P395e3trREjRmjnzp3nzJSXl6cHHnhAXbt2lbu7u0JCQjR9+nTt37//e6/f8ePHNXv2bPn6+iooKEjz5s1TYWGhjh07pkmTJsnX11cdOnTQvffeq5KSkkbLqq2t1cqVKzV27FiFhYXJ09NT/fr10+LFi1VfX9/ibNLXx7I/+eST6tWrl7y8vNSuXTtNmjRJH374oerq6iRJL7zwgmw2m8rKypSQkOBcFzc3t2bXMS0tTbNmzVJQUJBzLD8//zvnv9DrNWjJ5/TN74CXl5eGDBmidevWacyYMc7l33bbbY3Ww2az6Xe/+53zvf/m+G233XZR7/cPyWaz6bHHHtOSJUv0n//5n1qxYoXVkQCg9TMAAFwlxo8fb/r06WMqKiqsjnJBU6ZMMZLMlClTzK5du0xpaanZsmWL8fT0NIMHD240b1ZWlomMjDShoaFm7dq1pri42KSlpZnp06cbm81mXnnlFee8hw8fNgEBAaZTp04mPj7elJSUmC+++MLExcWZrl27GofD0WjZp0+fNl26dDGhoaFm/fr1pqSkxCQnJ5uRI0caDw8Ps2vXru+1ftOnTzd79+41paWl5s033zSSzIQJE8yUKVPMvn37TElJiVm6dKmRZB566KFGy1i7dq2RZJ599llz5swZk5eXZ1566SXj4uJiHnnkkYvK9ZOf/MT4+/ub+Ph4U15ebrKzs80jjzxiJJlt27Y1mtfb29sMHz78gus4cuRIs23bNlNWVmZ2795tXF1dTV5eXovzn+/1WvI5NfcdSE5ONmPGjDEhISFNvgPjxo0zLi4uJiMjo8nrDh061KxYseJ8b2mrsHDhQhMQEGBycnKsjgIArdnLFAQAgKtCcnKykWTi4+OtjvKdNGxcrl27ttH4bbfdZiSZvLw859j8+fONJPP22283mreystJ07NjReHp6muzsbGOMMTNnzjSSzPvvv99o3lOnThmHw9Fk4/Cuu+4ykppsBGZlZRmHw2EGDhz4vdZv/fr1jcb79OljJJmPP/640XhkZKSJiopqNLZ27VoTGxvbZNlz5841drvdFBcXtzhXZGSkGTZsWJPxnj17XnRBsGHDhmYfb2n+871eSz6nc30HcnNzjZeXV5PvwObNm40kc//99zca37lzp+nUqZOprq5uNlNrUlZWZoKCgsyiRYusjgIArdnLHGIAALgq7NixQ0FBQRozZozVUVpk8ODBje6Hh4dLUqNrva9atUqSdOuttzaa1+FwaPTo0aqoqNDmzZslSZs2bZIkjRs3rtG8HTt2VM+ePZu8/urVq+Xi4qKJEyc2Gg8LC1OfPn2UlJSkkydPXsyqSZIGDRrUJEdz4506dWpyffuJEyc2ewhF//79VVNTo0OHDrU4z/jx47Vr1y7dd9992r17t/OwgrS0NMXGxrZ4eZI0ZMiQZscvZf6WfE7n+g6EhISoV69eTZYdFxenfv366fXXX1dBQYFz/Pnnn9cvfvEL2e3275zTKl5eXpo0aZK2b99udRQAaNUoCAAAV4WCggLnMeBXEn9//0b33d3dJcl5jHpVVZWKi4vl4eEhX1/fJs8PDQ2VJGVnZ6uqqkolJSXy8PCQj49Pk3nbt2/f6H7Dsuvr6+Xv79/kePTPP/9cknT48OGLXj8/P79G911cXOTq6iovL69G466urk3OK1BcXKwnn3xS/fr1U2BgoDPXo48+KkkqLy9vcZ4lS5bozTff1NGjRzV69Gj5+flp/PjxzhLmYnh7ezc7fqnyt+RzutB34Fzn5Vi4cKHKy8v15z//WZKUnp6uf/3rX7rvvvu+U8bWoH379o0KDgBAUxQEAICrQmRkpE6cONHkRHdXOofDIX9/f1VWVja7bjk5OZK+/iXZ4XDI19dXlZWVKi0tbTLvmTNnmiw7ICBAbm5uqqmpkTGm2WnUqFE/zMpdwKRJk/TMM8/o3nvvVXp6uurr62WM0YsvvihJMsa0eJk2m03z5s3TRx99pKKiIq1evVrGGE2fPl1//OMfm8x7OfOf6/Va8jld6DuQm5vb7GvMmTNHoaGhevnll1VVVaU//OEPuuuuu1rliT7P5eDBg+rWrZvVMQCgVaMgAABcFW699Va5uLjolVdesTrKJTdt2jRJ0vr16xuNV1VVaevWrfL09HTuTj5hwgRJ/97NvEF+fr7S0tKaLHv69Omqra1VQkJCk8eee+45RUREqLa29pKsR0vU1dUpISFBYWFheuCBBxQSEuLcgK6oqLjo5QYEBCg1NVWSZLfbNXbsWOdVCb79/np5eam6utp5PyoqSsuWLfvB8p/v9VryOZ3rO5Cdna309PRmX9vhcOj+++9Xbm6u/vCHP2jFihV68MEHv9O6tgZpaWmKj4/XzJkzrY4CAK0aBQEA4KoQEBCgX/7yl3ryyScv6tj01mzRokWKjIzUwoULtW7dOpWUlCg9PV133HGHsrKytHjxYuehBs8++6zatWunhQsXasuWLSotLVVKSormzp3b7C7nixYtUvfu3XX33Xdr48aNKi4u1pkzZ/TXv/5VTz/9tF544YUml9u7HFxdXRUbG6vs7Gw9//zzys/PV0VFhbZt26alS5d+r2X/9Kc/1RdffKGqqirl5ubq97//vYwxuvnmmxvNd/311ys9PV2ZmZlKTEzU0aNHNWLEiB8s//leryWfU3PfgeTkZP34xz9WWFjYOTPff//98vT01H/+539qzJgx6tGjx3daV6tVVFTozjvv1PXXX6+pU6daHQcAWrfLfVpEAACsUl1dbW666SbTsWNHk5qaanWcZiUmJhpJjaYnnnjCGGOajN96663O5+Xn55uFCxeayMhIY7fbjb+/vxk3bpzZunVrk9dIS0szU6dONX5+fs7LJq5bt86MHj3auex77rnHOX9BQYF5+OGHTbdu3YzdbjchISEmLi7ObNmy5ZKt3549e5qML1q0yHzyySdNxn/7298aY4zJy8szCxYsMOHh4cZut5vQ0FAzf/588/jjjzvnbelVFvbv328WLFhgoqOjjZeXl2nXrp258cYbzSuvvGLq6+sbzZuammpGjBhhvL29TXh4uFmyZMk517G5f3K1NP+5Xq9BSz6nb34HvLy8zLBhw8zHH39sYmNjjZeX1znfn3vvvbfZq0y0VuXl5WbChAkmKCio1f43DwCtyMs2Yy7iAD0AAK5QZ8+e1YQJE5SSkqK33nqryZn/gatZr169VFFRoePHjzf7+PLly7VkyRLt3bv3MidruWPHjmnGjBk6fvy4Nm3a1OTKGACAJpZwiAEA4Kri5+enrVu3aurUqZo0aZLuvfdeFRUVWR0LuGyys7PVrl071dTUNBo/duyYjhw50uRQim9aunSpHn744R864vdSX1+vv/zlL7r22mtVW1urzz77jHIAAL4jCgIAwFXHw8NDy5cv15o1a7Rx40Z1795dzz33nKqqqqyOBlwWhYWFWrBggTIzM1VeXq7PPvtMs2fPlp+fn/7f//t/zvn+9re/adq0aSotLdXSpUtVWFioWbNmWZj8/LZu3arBgwfrgQce0P3336/PPvuMKxcAQAtQEAAArlqTJk1ScnKy7r33Xj311FOKiorS4sWLv/P15/FvNpvtgtNTTz1FtlYgLCzMeRnHm266SYGBgZo8ebKuueaaZjeoV69ercDAQP3lL3/RO++8Y8lJKS9k586dmjRpksaMGaN27dopKSlJ//M//yOHw2F1NAC4onAOAgAAJB0/flzPP/+8XnvtNfn4+GjevHm655571Lt3b6ujAWhGQUGB3nrrLb366qs6ePCg4uLi9MQTT+imm26yOhoAXKmWUBAAAPANOTk5euWVV7R8+XIdPXpUQ4cO1T333KNZs2bJ19fX6njAVa2+vl4fffSRXn31Va1Zs0YOh0OzZ8/WggULNHDgQKvjAcCVjoIAAIDmGGO0bds2vfrqq/rggw/k5uamCRMmaMqUKbrlllsUGBhodUTgqlBTU6Pt27frww8/1Jo1a5SZmamYmBjdc889mjlzpry9va2OCABtBQUBAAAXUlhYqJUrV2rVqlXavn276uvrddNNN2ny5MmaPHmyIiMjrY4ItClFRUXatGmT80SixcXFuu666zR58mTdcccdioqKsjoiALRFFAQAALTE2bNnnRsuGzZsUFFRkXr37q3Y2FiNHDlSI0eOVGhoqNUxgStKeXm5EhMTtX37dm3fvl2ffvqpjDEaOXKks4jr2rWr1TEBoK2jIAAA4GLV1NRox44dio+P18cff6ykpCTV1tZSGAAX8O1C4LPPPlN1dbV69OihkSNHavTo0Ro/fjyH8gDA5UVBAADApVJWVqbExETt3LlTCQkJ2rFjh6qrq9WhQwcNHDjQOQ0dOlTBwcFWxwUui9raWqWlpSkpKck57d27V1VVVerQoYNiYmI0ZswYxcXFsZcAAFiLggAAgB9KSUmJEhMTtWfPHu3Zs0d79+7VqVOnZLPZ1LNnTw0aNEiDBg1S//791bdvX4WEhFgdGfheysvLlZKSooMHDzqLgP3796uqqkq+vr4aOHCgBg0apMGDB2v48OHq1KmT1ZEBAP9GQQAAwOV0+vRp7d27V3v37nWWBvn5+ZKkkJAQ9evXT71791bfvn2df9nNGq1NVVWVvvzyS6WkpCg5OVmHDh3SoUOH9NVXX6m+vl4eHh667rrrNHjwYGchEBUVJRcXF6ujAwDOjYIAAACrZWVl6dChQ84NreTkZKWkpOjs2bOSpI4dO6pXr17q3r27evTooR49ejhvc4k3/FBqamp0/PhxZWRk6MiRI8rIyFBGRobS09N15MgR1dXVyW63KyoqSn369GlUanXv3l2urq5WrwIAoGUoCAAAaK2OHz/u/IX28OHDzg21kydPqr6+XpL0/9m787Cm7nwN4G8Swg4hIBA2EXBlERRwwwUrLr1Va7HbM120i3Y6z7293Rzv9D5t7Tq3tct05mp7tdPRTr0zbe1Ua1u1WreCoIALm6CIsoewJWELkOTcP3pzhgC2WoHD8n6e5zwJJyH5JpDl957f+Z6AgAC7wCA0NBRjx45FcHAwgoKC4OTkJPGjoKHKarVCq9WioqIClZWVKC8vtwsCysrKYDabAQA+Pj7i/9iECRMQGRmJqKgoTJw4EUqlUuJHQkRE/YQBARER0XDT0dGB0tJScSBnCw4uX76MiooKdHR0iNfVaDQICQlBcHAwQkJC7M77+fkhICAA7u7uEj4aGggdHR2oq6uDVqtFdXU1ysrKUFlZiaqqKrvzXV1dAAC5XA6NRoOIiAgxCOh+yt1ciIhGBQYEREREI03PrcK285WVlSgrK0NNTQ0sFot4fVdXV/j7+0Oj0cDPzw8ajQb+/v5igODn5wdvb294e3tDrVZzVoIErFYrmpqa0NTUhIaGBtTV1UGn06G6uhp1dXWora1FTU2NGAo0NTXZ/b6vry+Cg4MRHByM0NDQXucDAwPh6Ogo0aMjIqIhggEBERHRaGM2m1FbWwutVgutVou6ujrU1NRAp9NBp9PZna+rq+v1+25ublCr1VCr1WJoYDu1nXd3d4ebmxs8PT3h4eEBV1dX8fdcXV1HVchgtVphMBjQ0tKC1tZWtLa2Qq/Xo62tDa2trTAajTAYDGhsbBRDANt526ler+91u+7u7mKA4+fnh8DAQPj6+toFO/7+/ggMDISLi4sEj5yIiIYZBgRERER0bV1dXairq+s1YP258y0tLTCZTNe8XYVCAU9PT3h6esLV1RWurq7iOgBQqVSQy+VwcXGBs7Oz3WWenp52DfB+KnC41mW2QXtf+rrMaDTCYrGgvb0dJpMJFotFbCJpu8xkMqG9vR1dXV1oaWmBwWBAa2vrdT0PKpWqz8ClZ/BiO/X19YWrq+s1b5eIiOgXYEBAREREA8M20G5ubkZrayva2trQ1NQkbkXvOYju7OxEa2srAIhT5FtbW9HZ2SkOugFAr9ej+9cXg8EgNm3syTZ470vPoOGnLnN3d4dSqYSTkxNcXV0hk8ng5eVld5mjoyPc3Nx6hR9ubm7w8vKCu7s7XF1d4e7uDi8vL7i6usLZ2fkGn1UiIqIBs8VB6gqIiIhoZJLL5eLWb6lZLBY4ODhg9+7dWL16tdTlEBERDUlyqQsgIiIiIiIiIukxICAiIiIiIiIiBgRERERERERExICAiIiIiIiIiMCAgIiIiIiIiIjAgICIiIiIiIiIwICAiIiIiIiIiMCAgIiIiIiIiIjAgICIiIiIiIiIwICAiIiIiIiIiMCAgIiIiIiIiIjAgICIiIiIiIiIwICAiIiIiIiIiMCAgIiIiIiIiIjAgICIiIiIiIiIwICAiIiIiIiIiMCAgIiIiIiIiIjAgICIiIiIiIiIwICAiIiIiIiIiMCAgIiIiIiIiIjAgICIiIiIiIiIwICAiIiIiIiIiMCAgIiIiIiIiIjAgICIiIiIiIiIwICAiIiIiIiIiMCAgIiIiIiIiIjAgICIiIiIiIiIwICAiIiIiIiIiMCAgIiIiIiIiIjAgICIiIiIiIiIwICAiIiIiIiIiMCAgIiIiIiIiKx/JsoAACAASURBVIgAyARBEKQugoiIiKg/3X///Th//rzduosXLyIwMBDu7u7iOqVSiW+//RYajWawSyQiIhpqtjhIXQERERFRf5s4cSJ27drVa/3Vq1ftfo6JiWE4QERE9P+4iwERERGNOPfddx9kMtlPXkepVGLNmjWDVBEREdHQx4CAiIiIRpyIiAjExsb+ZEhgNptxzz33DGJVREREQxsDAiIiIhqRHnzwQSgUij4vk8vlmDNnDoKDgwe5KiIioqGLAQERERGNSPfeey+sVmufl8lkMjz44IODXBEREdHQxoCAiIiIRqSAgADMnTsXcnnfX3dWr149yBURERENbQwIiIiIaMR64IEHevUhUCgUWLx4MXx8fCSqioiIaGhiQEBEREQj1urVq3vNIBAEAQ888IBEFREREQ1dDAiIiIhoxFKr1Vi2bBkcHBzEdUqlEitXrpSwKiIioqGJAQERERGNaPfddx8sFgsAwMHBAatWrYK7u7vEVREREQ09DAiIiIhoRFuxYgWcnZ0BABaLBffdd5/EFREREQ1NDAiIiIhoRHN1dcUdd9wBAPDw8MDSpUslroiIiGhocvj5qxARERFJr7OzE62trTCZTGhvb0dbWxs6OjoAAM3NzTCbzb1+p7W1FZ2dnRg7diwAYMaMGdi7dy9kMhm8vLz6vB8vLy/IZDLI5XKoVCo4ODjAw8MDSqWSuyYQEdGIJhMEQZC6CCIiIhq5Ojs7UV9fj7q6OtTV1aGpqQlGoxEGgwEGg0E8bzttamqCwWBAV1cXjEYjurq60NLSIvXDEDk5OcHV1RUuLi5wdnaGp6cnVCqVeNr9vFqtFn9Wq9Xw9/eHn58f3NzcpH4YREREPW3hDAIiIiK6YVarFbW1taioqEB1dTXKy8tRV1cHnU4HnU6Huro61NfXo7a2Fnq9vtfv9zWYVqlUCA8PFwfVjo6OdlvuHR0d4ebmBmdnZ7i4uIgDdADiup5sg3kAeOWVV/Dcc89BoVBcM3SwWCwwGo12581mM5qbm8UZDB0dHWhraxNnMhiNRrvAo6KiolfYYbVa7e7HxcUFvr6+8Pf3h6+vL3x9fTFmzBgEBARAo9Fg7NixCAoKQlBQEJycnG7670VERHQ9OIOAiIiIejGZTCgtLUVJSQlKS0tRXl6O6upqVFZWoqKiAjU1Nejq6hKvb9sy7uvrC41GgzFjxsDX1xd+fn7iets6b29vSR6T2Wy2O9zhYGppaUFjYyO0Wq0YntTV1UGr1drNrqipqUFtbW2v59YWFtiCg7CwMIwfPx4RERFQq9WSPCYiIhpxtjAgICIiGqU6Oztx4cIFXLp0CSUlJbh8+TIuX76MkpISVFZWwvYVISAgQByYBgcHIzg4GEFBQQgJCeFW7gHQ1+yMqqoqVFdXo6ysDFVVVaioqBBDBG9vbzEssJ1OmDABkZGR1+yzQERE1AcGBERERCOd2WxGeXk5CgoKUFhYKJ7m5+eLTf7UajXCw8MRGRmJqKgohIeHIzw8HBMnToSHh4fEj4B6sv1NS0tLey1FRUVobW0F8OPfNTIyEvHx8YiKikJkZCTi4uLYbJGIiPrCgICIiGgkMZvNKCwsxOnTp5GVlYWsrCwUFBSgs7MTCoUCYWFhiImJQWRkpHg6ceJEzgAYQaxWqxgI5efno6CgQAyFTCYTZDIZwsLCkJCQgBkzZiAxMRHTp09naEBERAwIiIiIhrOqqiqcOHFCDAPOnDmDtrY2uLm5Ydq0aUhMTERcXByio6MxZcqUPhv50ehgsVhQWlqKvLw85Ofni/8ztbW1UCgUmDJlChITE5GYmIg5c+YgJiYGcrlc6rKJiGjwMCAgIiIaTrRaLX744QccPnwYaWlpuHDhAhQKBSZOnIj4+HhxmTFjBhwdHaUul4aB6upq5OTkiEtGRgYaGhrg4eGBmTNnIiUlBSkpKZg2bRoDAyKikY0BARER0VDW1taGQ4cO4ZtvvsHRo0dRUlICR0dHzJgxA8nJyUhOTsbs2bPFQ/kR3Syr1Yrc3FwcP34cR48exYkTJ9DU1AQfHx8sWLAAy5Ytw4oVK6DRaKQulYiI+hcDAiIioqGmtrYW+/btw759+3Do0CF0dHQgMTERKSkpSE5Oxpw5cxgI0KCxBQbHjh3DkSNH8P3338NkMiExMRG33347VqxYgejoaKnLJCKim8eAgIiIaChoamrC3//+d3zyySfIzMyEk5MTFi1ahJUrV3JrLQ0p7e3tOHTokBhi1dbWIjw8HPfeey/WrFmDiRMnSl0iERH9MgwIiIiIpGKxWPDdd99h586d2Lt3L+RyOVJTU5GamoqlS5dylgANeVarFadOncKePXuwa9cuVFVVISkpCWvXrsXdd98NT09PqUskIqLrx4CAiIhosBkMBmzZsgVbt25FdXW1OKC66667OKCiYctiseDw4cPYuXMnvvzyS8jlctx777347W9/i0mTJkldHhER/TwGBERERIOltrYWf/jDH7B161bI5XI8/vjjePjhhzF+/HipSyPqV3q9Hn//+9/x3nvv4eLFi0hNTcXvfvc7TJ8+XerSiIjo2hgQEBERDTSj0YhNmzbhgw8+gKenJ5566ik8/vjjnC1AI57VasWXX36J3//+98jJycGyZcvwzjvvYMqUKVKXRkREvW3hwWyJiIgG0BdffIHIyEj89a9/xZtvvokrV65g48aNIzIc+Pvf/w6ZTAaZTAZnZ+cBva+33npLvK/g4OABva/hUMdQJZfLsXr1amRnZ+PgwYOor69HXFwcnn/+eZhMJqnLIyKiHjiDgIiIaABUV1dj/fr1+Pbbb7F27Vps3rwZPj4+Upc1KFJSUpCWljYoA8C4uDjU19ejsrJywO9rONQx1FksFmzZsgXPP/88/Pz88OGHH2LBggVSl0VERD/iDAIiIqL+lpGRgYSEBFy6dAnHjh3DRx99NGrCAbo+7u7umDt37oi/z54UCgWeeOIJFBYWIioqCikpKXjvvfckrYmIiP6JAQEREVE/OnbsGBYvXoz4+HhkZWVh/vz5UpdENOQEBQXhyy+/xKuvvoqnn34aL774otQlERERAAepCyAiIhopCgsLsXLlSqxYsQJ//etf4eDAj1mia5HJZNi4cSP8/Pzw6KOPQqPR4PHHH5e6LCKiUY0zCIiIiPqB2WzGPffcg6lTp+Ljjz+WPBzYs2eP2DxPJpPh6tWruOeee+Dl5QUfHx8sX74cly9f7vV7DQ0NePrppxEREQFHR0eo1WrceuutOHr0aK/rFhUVYdWqVVCpVHBzc8O8efOQlpZ2zZrq6urwxBNPYNy4cXB0dISvry9SU1Nx7ty5m368RUVFuO2226BSqeDq6oqFCxciPT3d7jpmsxmffvopFi9eDI1GAxcXF8TExOC9996D1Wr9yefCyckJwcHBSElJwY4dO9De3v6T9XzyySd2z79MJoNWqxWbGra2tiI9PV28rOf/y/U+Vx0dHXjhhRcwefJkuLq6wtvbGytWrMBXX30Fi8UCANd9n1J56KGHsGnTJjz55JMoKCiQuhwiotFNICIiopu2fft2wdHRUSgpKZG6FDu33367AEC4/fbbhZMnTwotLS3CoUOHBBcXFyExMdHuujU1NUJYWJjg7+8v7Nu3TzAYDEJxcbGQmpoqyGQyYfv27eJ1L126JHh5eQlBQUHCd999JzQ3Nwu5ubnCkiVLhHHjxglOTk52t11dXS2EhoYK/v7+wjfffCM0NzcL+fn5woIFCwRnZ2fh5MmTv+jxxcbGCiqVSli4cKGQlpYmNDc3C1lZWcLUqVMFR0dH4dixY+J19+3bJwAQXn/9daGxsVGoq6sT/vjHPwpyuVx49tln+3wuNBqNsG/fPsFoNAparVZ45ZVXBADCu+++26uOoKAg8Wez2Sw8/fTTwuLFi4XGxsZedbu5uQlJSUl9PqYbea4effRRQaVSCd99953Q1tYmaLVa4dlnnxUACEePHr3u+5SaxWIRpk+fLqxcuVLqUoiIRrP/ZkBARETUD+bOnSvcf//9UpfRiy0g2Ldvn936O++8UwAg1NXVievWrl0rABD+9re/2V3XZDIJgYGBgouLi6DVagVBEIS77rpLACDs3r3b7rpVVVWCk5NTr4BgzZo1AgBh165ddutramoEJycnIT4+/hc9vtjYWAGAkJGRYbc+NzdXACDExsaK6/bt2yckJyf3uo37779fUCqVgsFgENfZnotPP/201/WXLVv2kwFBU1OTsHTpUuHf//3fBbPZ3GfdPzVYv5HnKiwsTJgzZ06v25g4ceKwCggEQRA+/fRTwcHBwe5/koiIBtV/cxcDIiKimyQIArKzs7F48WKpS7mmxMREu59DQkIA/Hg4Rpsvv/wSAHDbbbfZXdfJyQmLFi1Ce3s7Dh48CAA4cOAAAGDp0qV21w0MDMTEiRN73f+ePXsgl8uxfPlyu/UajQZRUVHIycn5xYcIdHZ2xsyZM+3WxcTEIDAwEOfPn0dNTQ0AYPny5X3uKhEbG4uuri676e225+LWW2/tdf39+/fjySef7LOW4uJizJw5E3K5HH/4wx+gUChu+PHcyHO1bNkynDx5EuvXr0dmZqa4W0FxcTGSk5Nv+L6ltGTJEpjNZpw5c0bqUoiIRi0GBERERDepo6MDJpMJarVa6lKuSaVS2f3s6OgIAOK+9x0dHTAYDHB2doaHh0ev3/f39wcAaLVadHR0oLm5Gc7OznB3d+91XT8/P7ufbbdttVqhUql67ZtvGxBeunTpFz02Hx8fyGSya9ah0+kAAAaDAS+88AJiYmKgVqvF+9+wYQMAoK2t7bqei2tpamrCqlWrEBwcjP379+OTTz654cdyo8/Vli1b8PHHH6O0tBSLFi2Cp6cnli1bJgYcw4mnpycUCgX0er3UpRARjVoMCIiIiG6Ss7MzfHx8+mz6N1w4OTlBpVLBZDKhubm51+W1tbUAftyK7eTkBA8PD5hMJrS0tPS6bmNjY6/b9vLygoODA7q6uiAIQp/LwoULf1HtBoOhz/W2YMAWFKxYsQKvvPIK1q1bh4sXL8JqtUIQBLz77rsAfpwJcj3PxbU4ODjg8OHD2Lt3L2JiYrBu3TpkZWX1ed2+Ag3bfd/IcyWTyfDAAw/g8OHD0Ov12LNnDwRBQGpqKt55553rus+h4sqVK7BYLAgKCpK6FCKiUYsBARERUT9YsmQJPvvsM6nLuCl33HEHAOCbb76xW9/R0YHvv/8eLi4u4i4Ftqn3tl0NbOrr61FcXNzrtlNTU2E2m3sdWQAA3njjDYwdOxZms/kX1d3S0oLz58/brcvLy0N1dTViY2MREBAAi8WC9PR0aDQaPPHEE/D19RUHzH0dkcD2XHz77be9Lps2bRqeeuqpXus9PDwQFBQEd3d3fPXVV3B3d8eqVavEXRy6c3V1RWdnp/jzpEmTsG3bNgA39lx5eXmhqKgIAKBUKrF48WLxCBY9/44/dZ9Dwaeffgpvb2/MmDFD6lKIiEavwe97QERENPJkZGQIMplM+OKLL6QuxY6tSWF7e7vd+o0bNwoAhLNnz4rreh7FwGg02h3FYNu2beJ1S0pKBG9vb7ujGBQUFAhLly4V/Pz8ejUprK2tFSIiIoTw8HDh22+/FfR6vdDQ0CB88MEHgqura5/NAK9HbGys4ObmJsydO1fIzMwUWlparnkUg1tuuUUAILz55ptCXV2d0NbWJhw5ckQYO3asAEA4dOhQr+ciICBA+PrrrwWj0ShUVFQIjz/+uODv7y+UlZX1qqP7UQwEQRCOHTsmKJVKYdasWYLJZLK7bNmyZYJKpRLKy8uFkydPCg4ODkJhYeENP1cqlUpYsGCBcP78ecFkMgm1tbXCpk2bBADCq6++et33KTWtViuo1WrhP//zP6UuhYhoNONRDIiIiPrLI488IqjVauHSpUtSlyJkZGQIAOwW2+Cr5/rbbrtN/L36+nrhySefFMLCwgSlUimoVCph6dKlwvfff9/rPoqLi4VVq1YJnp6e4mETv/76a2HRokXibT/yyCPi9RsaGoSnn35aCA8PF5RKpeDr6yssWbLEbmB+vTZv3izeR1BQkHD69Glh4cKFgru7u+Di4iIsWLBASEtLs/uduro64bHHHhNCQkIEpVIp+Pv7C2vXrhX+4z/+Q7yt7kcI6PlcBAQECPfee69w8eJF8Tp/+9vfej2f7777bp/P/3333Sf+XlFRkTBv3jzBzc1NCAkJEbZs2WJX6/U+V+fOnRMee+wxYcqUKYKrq6vg7e0tzJo1S9i+fbtgtVrtrvtz9ymVjo4OITk5WRg/frxgNBqlLoeIaDT7b5kg/P8Od0RERHRT2tvbsWDBAmi1Whw5cgTjx4+XuiSiIc1kMuHuu+/G8ePH8cMPP2Dq1KlSl0RENJptYQ8CIiKifuLi4oKDBw9Co9Fg1qxZOHz4sNQlEQ1ZVVVVWLBgAX744Qd89913DAeIiIYABgRERET9SK1W48SJE1i+fDmWLFmCBx98EA0NDVKXRTRkCIKAjz/+GLGxsdDr9Th58iRmzpwpdVlERAQGBERERP3O2dkZO3bswKefforvvvsO0dHR+Pjjj6Uua1iQyWQ/u2zatEnqMukXunjxIlJSUvDII4/gvvvuQ05ODqZMmSJ1WURE9P8YEBAREQ2Qu+66CxcuXMDy5cuxdu1azJs3D/v37wfb/1ybIAg/uzAgGH6Ki4vxyCOPIDo6Gq2trcjOzsZ7770Hd3d3qUsjIqJuGBAQERENILVaje3bt+PkyZPw9PTEbbfdhunTp+PTTz+FxWKRujyiAZWTk4O77roLkZGRSE9Px7Zt23Dy5EnExsZKXRoREfWBAQEREdEgmDVrFr755hucOXMGkyZNwn333YdJkybh1VdfRXl5udTlEfWblpYW7Ny5E8nJyUhISMCVK1fw2WefobCwEGvXroVczq+fRERDFQ9zSEREJIFLly5h69at2LVrFxoaGnDLLbdgzZo1WL16NVxcXKQuj+iGCIKAEydOYMeOHdi9eze6urqwYsUKrF+/HosXL5a6PCIiuj5bGBAQERFJqKurC9988w3+8pe/YP/+/XBxccGtt96KlStX4tZbb4VarZa6RKI+dXV14cSJE/jqq6+wd+9elJWVIT4+HmvWrMGvfvUr+Pj4SF0iERHdGAYEREREQ4VOp8Onn36KvXv34sSJExAEAfPnz8fKlSuxcuVKhIWFSV0ijXIGgwEHDhzA3r17sX//fuj1ekRHR2PlypW49957ERMTI3WJRET0yzEgICIiGopaW1tx5MgRfP7559i3bx/0ej0CAgIwd+5cpKSkYOnSpQgNDZW6TBrhWltbkZGRgbS0NKSnp+PEiROwWCyYNWsWVqxYgdtvvx2TJ0+WukwiIuofDAiIiIiGuq6uLqSlpeHo0aM4duwYTp06hc7OTkyYMAFxcXG4/fbbMWPGDIwfPx4ymUzqcmkYq6urQ1ZWFn744QccO3YM2dnZMJvNiIyMRHJyMpKTk5GSksJdX4iIRiYGBERERMNFdXU1cnJykJmZiSNHjuD8+fNob2+HUqlEV1cXvLy8kJiYiMTERCQkJCAxMRHBwcFSl01DlNFoRE5ODrKzs3H69GlkZ2fj6tWrAIDJkyeLgUBycjL8/f2lLZaIiAYDAwIiIqKhSK/XIysrC6dPn8bp06eRlZWFmpoaKBQKTJkyBTNmzEBiYiJmzpyJSZMmIS8vD1lZWeJSXFwMq9WKgIAAxMbGIiYmBlFRUYiOjsaUKVPg6uoq9UOkQWKxWHD58mXk5eWhoKAA+fn5+OGHH6DT6cT/EVuwZFu8vb2lLpuIiAYfAwIiIiKpmc1mFBcXIycnB+np6UhLS0NRUZE4eIuPjxeXuXPnXtf0bqPRiDNnziArKwu5ubkoKChAYWEhOjo6IJfLERYWhpiYGERGRmLKlCmIiIjA+PHj4evrOwiPmAZCa2srSkpKcPnyZVy6dAn5+fkoKCjAhQsXYDKZxL/7+PHjcfz4cZhMJvj5+WHJkiVYtGgRFi1ahJCQEKkfBhERSYcBARER0WArLS1FWloacnJyxMVkMsHT0xMxMTFiEDBv3jxoNJp+u1+LxYKSkhJx4Gg7LSkpQWdnJwDA09NTDAsiIiLE8yEhIQgKCoKzs3O/1UM3xmq1QqvVory8HFeuXMHly5fFQKCkpARarRYAIJPJEBISgilTptjNHImMjBRnjlgsFpw7dw6HDx/G4cOHkZaWBpPJhPDwcKSkpCAlJQW33HILD1VIRDS6MCAgIiIaSLa+AbYlIyMDDQ0NUCqVmDBhAubOnYukpCTEx8djypQpkMvlg16jxWJBRUUFLl++bLfYBp8tLS3idf38/BAUFITg4GAxNAgJCUFISAj8/Pzg6+uLMWPGsFniDWpuboZOp0NtbS2qq6tRWVmJ8vJyVFVVoaqqCuXl5aipqYHZbAYAKJVKjBs3Tgxxuoc64eHhNxzkmM1mnD9/XgwMjh8/DovFgsmTJ9sdOcPT03MgHj4REQ0NDAiIiIj6S3NzM86fPy+GAenp6SgtLQUAhIeHi0FAfHw8EhIShs3W+NraWlRWVqKqqgplZWXioLX7+Y6ODvH6CoUCY8aMEcMCf39/+Pr6wsPDA8HBwVCr1fD09IRKpeq1DHdtbW0wGAy9Fr1ej5qaGjQ2NqKxsRH19fWora2FTqdDfX09TCaTeBtyuRwajQbBwcHi0jOMCQoKgoODw4A9jpaWFmRmZoqBwZkzZ6BQKBAbGyvOMJg/fz4cHR0HrAYiIhp0DAiIiIh+iRvpG5CUlDTim77V1tairq4OdXV10Gq1qK+vR319Perq6lBZWYmsrCw0NDTA09MTBoNB3BLek5eXlxgWKJVKeHl5QSaTQa1WQyaTwcvLC3K5HCqVCg4ODvDw8BB/193dHUqlstdt9tWzoaWlBV1dXb3WNzU1iedNJhPa29vR1taGjo4OtLa2orOzU/zd5uZmmM1mNDU1wWAwwGg09nmbtsdltVphsViQkJCA8PBw+Pn5wc/PTwxTfH194e/vD41G0+fjkFJtbS1OnDiBw4cP4+DBgygrK4Obmxtmz54tBgbTp0/nzBEiouGNAQEREdH1qK6uFoOA7n0DPDw8MHXqVDEMmDdvHsLCwqQud8hIT0/H2rVrYTAY8P7772P16tUAem9pNxqN0Ov10Ov14rquri4YDAZYrVY0NTVBEATo9XpYLBZxMN599we9Xo+eX2vMZjOam5t71eXs7AwXF5de6z08PMQt805OTnB1dYWLiwucnZ3h6uoKJycnMYjw9PSEQqGAWq22mwXR1+wI4MfeEw899BAyMjLw3HPP4fnnn4dCoei353owlZaWirMLvv/+ezQ2NsLPzw8LFixASkoKlixZgnHjxkldJhER3RgGBERERD3V1NQgOzu7V98ABwcHTJw4UWwimJSUJFnfgKGuvb0dL730EjZv3oxly5Zh+/btCAwMlLosyVmtVvzpT3/Cxo0bMW3aNOzYsQOTJk2SuqybwoaHREQjBgMCIiIa3VpaWnDu3Dm7RoKFhYUAgICAALsmgsOpb4CUTp06hTVr1kCr1eLNN9/E+vXrpS5pyCkoKMCaNWtQWFiIF198ERs2bBgxQRMbHhIRDVsMCIiIaPTo3jfA1jvg7NmzffYNmDNnDrd43qCuri689tprePXVV7Fo0SL8+c9/RnBwsNRlDVlmsxlvv/02XnjhBcyYMQM7duxARESE1GX1OzY8JCIaNhgQEBHRyGU7xKCtd8CZM2fQ3t7eq2/A3LlzER4eLnW5w1peXh7WrFmD4uJivP7663jiiSfYsO465ebm4sEHH8SVK1ewefNmrFu3bkQ/d2x4SEQ0ZDEgICKikUGr1SIrK0ucHZCZmYn6+nr2DRhg3beCJyQkYMeOHZgwYYLUZQ07JpMJmzZtwltvvYXFixfjww8/RFBQkNRlDQo2PCQiGjIYEBAR0fBzI30D4uPj++xWTzfvwoULWLNmDfLy8rBp06YRtR+9VDIyMrB27VrodDq88cYbo65/AxseEhFJigEBERENbX31DTh37hwsFgv7BkhEEARs374dTz31FKKjo7Fz505MnjxZ6rJGjO5HgFi9ejW2bt2KMWPGSF2WJNjwkIhoUDEgICKioeVafQPc3d0RGxtrFwhERUVJXe6oc+XKFTz88MNIT0/H008/jVdeeQVKpVLqskakw4cP4+GHH0ZHRwc++OAD3HHHHVKXJDk2PCQiGlAMCIiISDp6vR7Z2dlIS0tDTk4OTp06hbq6Oru+AbbeAdOmTeP0dQnZZg0888wzGDduHHbu3Inp06dLXdaIZzQasWHDBmzbtg133XUXPvjgA3h7e0td1pDBhodERP2KAQEREQ2O1tZWnD171q5vwIULFyAIgrirQPfeAewbMHTU1NRg/fr1OHDgAJ555hm89NJLcHJykrqsUWX//v149NFHIQgCtm3bhuXLl0td0pDEhodERDeFAQEREfU/i8WCoqIiuzAgKysLnZ2d0Gg0SEhIEGcHzJ49e9TuXz0cfP755/j1r38NtVqNHTt2YO7cuVKXNGrp9Xo88cQT+OSTT7Bu3Tq8/fbbcHd3l7qsIYsND4mIbhgDAiIiunm2vgG23gEnT55EW1sb+wYMYzqdDo899hj27t2LdevW4Z133oGbm5vUZRF+DG1+85vfwN3dHX/5y1+QnJwsdUnDAhseEhH9LAYERER0YwwGA/Ly8sQmgqdPn4ZOp+uzb0BcXBwUCoXUJdMN6j4A/eijj7Bw4UKpS6Ieamtr8dhjj+Grr77CunXr8O6778LV1VXqsoYVNjwkIuqFAQEREV3bjfQNmD59Ogcow5xer8fGjRuxbds2PPDAA9iyZQs8PDykLot+wueff47HHnsMGo0GO3fuRGJiotQlDVtseEhExICAiIj+30/1DfDy8kJCQoLYQHDWtYt/zwAAIABJREFUrFnw9fWVumTqRwcOHMCjjz4Ki8WCbdu2YcWKFVKXRNepvLwcDz/8MI4fP45nnnkGL7/8Mrd69wM2PCSiUYgBARHRaHWtvgFubm6Ii4uz6xsQGRnJrWYjFA+jNzJ0PwxlWFgYdu7ciWnTpkld1ojBhodENEowICAiGg2u1TdAoVBg0qRJdmHAjBkzuPVxlEhLS8PatWvR3NyM999/H6mpqVKXRDfpypUreOihh3Dy5Ek899xzeP7559kHZACw4SERjVAMCIiIRpquri7k5uYiLS3tmn0DbL0D5syZw74Bo1B7ezteeuklbN68Gampqdi6dSt3GRlBrFYr/vSnP2Hjxo2Ii4vDzp07MWnSJKnLGtHY8JCIRggGBEREw1lffQOys7PR0dEBlUqF6OhosYngzJkz4efnJ3XJJLHMzEysWbMGtbW1ePPNN7F+/XqpS6IBUlhYiDVr1qCgoAAvvvgiNmzYALlcLnVZowIbHhLRMMWAgIhoOOneNyAnJwdpaWnQ6/XsG0A/y2QyYdOmTXjrrbeQkpKCDz/8EMHBwVKXRQPMbDbj7bffxgsvvIDExETs2LED48ePl7qsUYcND4lomGBAQEQ0VBmNRuTm5opNBE+cOIHa2lr2DaAblpubizVr1qC0tBSbN2/GunXrGB6NMrb/gcuXL+Ott97i/4CE2PCQiIYwBgRERENBV1cXLl68KDYR/Km+AbNnz4abm5vUJdMwwK3H1F1XVxdee+01vPrqq5xFMoSw4SERDSEMCIiIBhv7BtBg4P7ndC2ZmZlYu3YttFot+1AMQWx4SEQSYkBARDTQevYNSE9PR1NTE5RKJaZOnYqkpCT2DaB+ww72dD26H8ni1ltvxfbt2xEQECB1WdQHNjwkokHEgICIqD/17Bvwww8/QKvV9tk3IDExEU5OTlKXTCPIlStXsHbtWmRkZOC5557D888/D4VCIXVZNISlpaVh7dq1aG5uxvvvv4/U1FSpS6KfwYaHRDSAGBAQEf1SffUNKCoqgtVqtesbYOsdoFarpS6ZRihBELB9+3Y888wzCAsLw86dOzFt2jSpy6Jhwmg0YsOGDdi+fTvuvPNOfPDBB/D29pa6LLoObHhIRP2MAQER0fUqLS0VgwDbYjKZ4OnpiZiYGDEImD9/Pvz9/aUul0aJ8vJyPPzwwzh+/DieeeYZvPzyy9w3mX6RAwcO4NFHH4XFYsG2bduwYsUKqUuiG8SGh0R0kxgQEBH1pWffgJMnT6KxsRFKpRITJkwQmwiybwBJ6fPPP8djjz2GgIAA7NixA4mJiVKXRMOcXq/Hxo0bsW3bNjzwwAPYsmULPDw8pC6LfiE2PCSiG8SAgIioubkZ58+fF8OAtLQ0XLlyhX0DaMiqra3FY489hq+++grr1q3Du+++C1dXV6nLohHk888/x29+8xu4u7vjo48+wsKFC6UuifoBGx4S0c9gQEBEo4vZbEZxcbHYRDAtLY19A2hYsQ3cPDw88NFHHyE5OVnqkmiE0ul0+PWvf409e/Zg3bp1eOedd+Dm5iZ1WdSP2PCQiHpgQEBEI9v19g2YN28eNBqN1OUSXZNer8e//du/YdeuXVi3bh3efvttuLu7S10WjQKff/45fv3rX0OtVmPHjh2YO3eu1CXRAGDDQyICAwIiGkl69g3IyMhAQ0NDn30DpkyZArlcLnXJRNdl//79ePTRR8WjFdx2221Sl0SjTE1NDdavX48DBw7gmWeewUsvvcTdrUY4NjwkGpUYEBDR8NSzb0B6ejpKS0sBAOHh4WIQEB8fj4SEBDg7O0tcMdGNsx1+btu2bbjrrrt4+DmSVPfDaY4bNw47d+7E9OnTpS6LBgkbHhKNCgwIiGjou5G+AUlJSRxA0Yhw6NAhPPLII+jo6MD//M//YNWqVVKXRAQAuHr1Kh566CGkp6fj6aefxiuvvAKlUil1WTTI2PCQaERiQEBEQ091dbUYBOTk5ODMmTNob2+Hh4cHpk6dKoYB8+bNQ1hYmNTlEvWr9vZ2vPTSS9i8eTNWr16NrVu3YsyYMVKXRWTHNpvgqaeeQnR0NHbu3InJkydLXRZJiA0PiUYEBgREJK2amhpkZ2f36hvg4OCAiRMnik0Ek5KS2DeAhjWLxYK9e/ciNTX1mtfJyMjA2rVrodPp8MYbb2D9+vWDWCHRjbtw4QLWrFmDvLw8bNq0Cc8++ywUCoXUZZHE2PCQaNhiQEBEg6elpQXnzp2zayRYWFgIAAgICLBrIsi+ATTSvPzyy3j55ZeRmZmJhIQEu8tMJhM2bdqEt956C4sXL8aHH36IoKAgiSolujFmsxlvv/02XnjhBSQkJGDHjh2YMGFCn9d97bXXMHXqVKxYsWKQqyQpseEh0bDBgICIBkb3vgG23gFnz57ts2/AnDlzuBWBRrTjx4/jlltugSAIGD9+PHJzc8UALDc3Fw8++CCuXLmCzZs3c9YADVt5eXlYs2YNiouL8frrr+OJJ56w2//8/PnzSEhIgIeHB/Lz8xEYGChhtSQlNjwkGrIYEBBR/7AdYtDWO+BafQPmzp2L8PBwqcslGjSNjY2Ijo6GTqeDxWKBg4MDnnzySfz+978Xt7rOmDEDO3bsQEREhNTlEt2Urq4uvPbaa3j11VexaNEifPjhhwgJCUFnZyemTZuGixcvAgBmzpyJ48ePc3cEAsCGh0RDCAMCIrpxWq0WWVlZ4uyAzMxM1NfXs28AUQ+CIGDFihX47rvv0NXVJa6XyWSIiopCaWkp3nzzTfzmN7/hF18aUU6dOoW1a9eipqYGb775JsrKyvDGG2/AYrEAABQKBZ5//nm8+OKLEldKQxEbHhJJhgEBEf20G+kbEB8fDxcXF4krJho6Nm/ejI0bN6LnR61CoYCXlxe+//57xMbGSlQd0cBqa2vD7373O/z1r3+F0WgUwwEbmUyGQ4cOYdGiRRJVSMNBz4aH6enpaG9vZ8NDooHBgICI/qmvvgHnzp2DxWLp1Tdg9uzZPPQa0U/IysrCnDlzYDab+7zcwcEB69atw9atWwe5MqLBYzKZEB0djatXr/YKCGxBWX5+PjQajUQV0nDDhodEA4oBAdFo1rNvwNmzZ9HW1gZ3d3fExsbaBQJRUVFSl0s0bOj1esTExECr1V4zIAB+3IL67bffYtmyZYNYHdHgefLJJ7Fly5Zrvg6USiUWLFiAgwcPcnc0+kXY8JCoXzEgIBot9Ho9srOzkZaWhpycHJw6dQp1dXV2fQNsvQOmTZvGL2pENyE1NRVff/21Xd+Bvsjlcvj7++PChQtQqVSDVB3R4EhPT8e8efN67WLTk1wux3/9139hw4YNg1QZjWRseEh0UxgQEEmls7MTV69excSJE/v9tltbW3H27Fm7vgEXLlyAIAjirgLdewewbwBR/9m6dSv+9V//9ScHRXK5HHK5HGazGb6+vvjjH/+Ie++9dxCrJBpYLS0tiI6ORnl5+c8GBMCPuxukpaVh1qxZg1AdjSYD3fCwoaEBr7/+Ol599VV+n6KRgAEBkRSOHDmC9evXY86cOfj4449v6rYsFguKiorswoCsrCx0dnbCy8sLCQkJYhDAvgFEAysvLw8JCQno7OzsdZmDgwPMZjOcnJwwe/ZsLFu2jFuyaMSqqqrCjh07cPDgQWRmZqKrqwsuLi5ob2/v8/oKhQIajQZ5eXlQq9WDXC2NFgPR8HD37t246667EBERgV27dmHmzJkD+AiIBhwDguGsq6sLLS0tAICmpiYAPyb2timt3S/vi16vv2aqL5PJ4OXldc3fdXd3h1KpBAA4OjrCzc0NAMQP9e6X0z9VV1fjqaeewmeffQaZTIawsDBcvnz5hm+jexPBkydPsm8ADQsdHR1oa2sD8GN3846ODgBAc3Oz3f7J3S+7lvb2dphMpp+9T09Pz589znrP67i5uYn7qna/7OcGLS0tLYiNjcWVK1cgCAKUSiW6urogl8sRHx+Pf/mXf0FKSgpmzpzJ90caVdrb25Geno7Dhw9j//79yMvLA/BjaNZ9NxylUomUlBR88803Nx2a2d5HjEYjurq6YDAY7Nb3ZPse1VNfr3tnZ2dxS7FKpYJSqYSnp6fdehoe+qPh4eOPP44///nPsFqtEAQBv/3tb7Fp0yY4OTkN4iMZfK2trejs7ITBYIDFYoFerwfwY2PSvoLAnp/1Nq6urn0+VyqVCnK5HEqlEu7u7nBxcYGzszM8PDzg4ODQ/w+IbBgQDJbm5mY0NjaKi9FoRGtrK9ra2tDU1ITW1la0traipaUFer1e/Lm5uRkGgwFWq1U8vZ4vz0OFk5MTXF1dIZfLoVKpoFAo4OnpCQ8PD7i5ucHNzQ1eXl5wd3cXf1ar1eJ5T09PeHt7w9vbG2q1Gh4eHlI/pF/EbDZjy5YteO6552A2m8WtizKZDI2NjdcMYwwGA/Ly8sQmgqdPn4ZOp+uzb0BcXNzPDoSI+tLc3Ayj0QiDwQCj0YiWlhYYjUZ0dnbCaDSKH/bXWmf78m1bJwiC+EXBbDajublZ4kfYv2zvY4B9QKrX69HY2Ajgx5AhMDAQYWFhCA0Nhbe3t/jlRqVSwdHRER4eHnBxcYGrqytUKhW8vLzg6ekJT0/PEf/FkkY3nU6HI0eO4PDhwzhw4ACqqqrg4OAAq9UKq9WKP/3pT/jVr36F2tpa6HQ6NDQ0QK/Xo6mpye60+3mTyYSmpqaf3TgyGGwDGLVaDWdnZ6jVanh5eYmn3c+r1Wp4e3vDz88PAQEBP7lxhgbeL2l4GBoaivLycvFnBwcHhIeHY9euXUhISJDiYfyszs5O6HQ6aLVa1NfXi6+nnq8rvV4Pg8EgvsaMRqNd2C8ltVoNBwcHcUxhe21da/H29oavry/8/Pzg6+srdflDGQOCX6K1tRXV1dXQ6XTQ6XSorq5GQ0MDmpqa7EIA22L7wOrJNnjuPiB2d3eHSqWyGyDbPmhsp7aEuvuXVFvK1jOF+6ktXt23kvXU2dmJ1tbWPi/r/uUf+GdSaLFYYDQaAaBXmGH7wLYNFgwGgxiQGAwGNDc39wpM+pqiq1QqxcDAFhp0/9nHxweBgYHw9fWFv78/AgICxC/vUklLS8P69etRXFwMq9Xa6/LDhw9j0aJFN9Q3YPr06XB1dZXg0dBQ1NraioaGBjQ2NqK+vh4NDQ1oaGiAwWAQP9htg//ui+2ya30M2N5jbANbT09PKJVKqFQq8X3Iw8MDSqUSXl5edoNl23tS99lItvcx4J/vf0DvrQfd39uu5Xqu0/096Uau030rh+35sYW0gP3sLNv738WLF3Hq1CmEhISIu/F0dHRAr9ejq6sLzc3N4qwHg8GArq6un6zNyclJDAu8vLygUqmgUqnEdZ6enlCpVPDx8YGPj4/4/mc7zxkKNFS1tLSgrKwMV69eRXl5OWpqalBUVISioiJUVlZe8z3Jw8PjmgNsLy8vuLi4wMvLq9fWRk9PTzg4OIjvQ7bL+7r9nlslrxVwdp+t2dTUJF7P9n5gu7ypqUkMLvoKOJqamnqFGU5OTvDz80NQUBB8fX2h0WgQEBCAgIAAjB07FuPGjUNoaKjk321Gi59reBgdHY3ly5f3+j1b4LVhwwa8/PLLg3YEhc7OTlRUVKCiogLl5eWoqKiATqdDbW0ttFqteN4WZtvYDjna8/XVfbF9Ltk+v22f3baZdrYxR/fP+u5sr8mejEZjr0Ogdv9sto1JbGMK22e0Xq+H2WwWN7r2DDm6Lz1fx0qlUgwLAgIC4OfnJ44bQkJCxCUgIOCm/h7DFAOC7pqbm1FeXo6ysjJUVFSgpqYGtbW1qKmpQV1dHbRaLbRaba/UbMyYMRgzZsxPDlx7LrYPLLo22xfonmFLXyGMbbENjrpzc3ODRqOBv7+/3RtBYGAgQkJCEBoairFjx/b5heFmNDY24sUXX8SWLVugUCj6nFbl6OiImTNnwmAwoLCwEGazGf7+/khMTMSMGTPEU29v736tjYa2+vp68cO8pqZG3ILWffBvCwQaGhp6TbWXy+Xw8fERB5VqtdpuYNl9gNnXZbagkkeyGHgmkwltbW12IU7PMOdalxkMBjQ0NPQ5gPH09ISPjw/GjBnTZ4gQFBQkvh9qNBoGjtRvLBYLrly5guLiYly8eBFXr15FWVmZ+P2q+8BErVYjMDBQHAT7+flBo9Ggra0NFosFd999N3x9fTFmzJgRO0PObDajoaEBOp1O/N5ZW1srboiyrauqqrLbOOPj4yN+fwkNDUVYWBgmTpyISZMmITQ0dMQ+X1Lrq+GhXC7vcwMQ8OPAe8KECfjf//1fTJs27abv32q1oqysDBcvXsTFixfF11ZFRQXKysqg1WrFgM3Z2RlBQUHQaDTw9fVFYGAg/Pz8eg2K/fz8hu0M3etlsVjQ0NCAuro6u7Fd9+DENs6rqakRn0MnJyeMHTtWDAzGjRuHcePGYfLkyZgwYcIN9aoYRkZXQFBTU4PLly+LAUD3F1RFRYXdG69KpRLTW9upLVny9fVFQECA+KLilpqhpbOzE3V1deKHavcPWNt5nU6HyspKuy/WarUaISEh4oet7c0gNDQU48ePh7+//3Xdv9VqxSeffIInnngCra2tP3kMdLlcjvDwcKxatQozZszAjBkzEBoaetPPAQ09VqsVWq0WV69ehU6nQ1VVlfj/qNVq7b4Qdp894+joCD8/PzGE9PX17XOLse287WcaPTo7O+0Co+6LLVTqeZlOp7O7DQ8PD/HLoy008Pf3F9fZ3g85/Zls2trakJ+fjwsXLqC4uBjFxcUoKipCSUmJ+B6m0WgQHh4ufq6OHTvWbiv4SB+U9Dej0YiysjK7pby8HOXl5SgtLUVtbS2AHwc1EyZMwKRJk8RlypQpiI6OZo+EfmSxWLB8+XIcPnz4J7/rOTg4QBAEPPvss3jllVeua9xgNBpRUFCACxcu4NKlS2IgcOnSJXE3Y19fX4wbN04cwNpeY7b3a41G02+PdTTp7OxEZWWlOEa0vcZsY8fS0lJxw4yPjw8mTpwoBgYTJ05EZGQkJkyYMJw3BI+8gKCpqQmlpaUoLS1FQUEBCgsLUVpaikuXLolTVZRKJcaMGYPAwECEh4cjPDwcAQEBdj+zg+7o0N7ejpqaGvF/prq6Wvy5uroaV69eFWeMODk5ISgoCJGRkYiKihL/V8LDwzFu3DjI5XKcOXMG69atw7lz566ZJvc0ZswY1NXVDeTDpEFwI/9LwI/JfmBgoPjeExAQIG5N675Oo9FwSz4NiKamJvH/tPtpU1OTeL6qqkrctQL45/9tX5+bAQEBCA0N7ffZWCS9pqYmFBQU2O0CV1xcDIvFAkdHRwQHB9t9NkZGRmLq1Kk/uxsQ9S+DwYCSkpJe34OLiorQ2toKhUKB0NBQREZGij2MEhMTOZD8hQRBgJ+fH+rr66/r+gqFAlOnTsWuXbswZcoUcb2t+XRhYaH4OisqKoLVau3z9RUeHo7o6Gj+3SRke0+0jTNtrzfb+6JSqcSECRPEhuGRkZGYOXMm/Pz8pC79egzfgKCsrAwFBQXIy8tDfn4+8vPzcfHiRfELuJubG8aPHy8uEyZMEM8HBgbykFJ0XaxWK6qrq3Hp0iWUlJTYLZcuXRK7tNr2xbreD4meKioqEBwc3J+lUz+zWq2oqKjo9X9QUlKCq1ev2u1H6u3tLc5GsaX5wcHB4syUgICAQdsfkehmGY1GVFZWiltQKisrxZl3tqX7bi4BAQEYN26c3WewbeHslqGvs7MT2dnZyMjIQFpaGk6dOoWamhoAQHBwMOLi4hAbG4u4uDhMmzYNYWFhDDGHOIvFgtLSUpw7d85uqa6uBgAEBQVh1qxZSEpKwuzZsxEfH8/ZsdchPz8fMTExN/x7Dg4OmD9/PiwWC86ePQuj0Qi5XI6IiAjExsYiNjYWU6dOxdSpUxEaGsoxyzDS0dGBwsJC5Obm4vz588jNzcW5c+fE3Z9DQ0MxY8YMzJ49G7Nnz8b06dOH4vfBoR8QtLa24syZM8jNzbULA2xbNIKDgxEdHY2YmBhMmjRJDAMCAwMlrpxGg8rKSpSUlGDXrl04e/YsysvLxYZF3Tk4OEAmk/XZrFImk+GLL77AHXfcMVhl00+oqalBfn5+r1CotLRUnNanUqnsBj3jxo2z2yWFzaNotKmtrbVrjFVaWiq+dq5cuSK+93l7e4uvm4iICEyYMAGTJ09GZGQkXzcSaW5uxrFjx5CWloaTJ08iOzsbJpMJfn5+mDNnDubMmYNp06YhLi5ObMBJI4NOp8O5c+dw5swZZGRk4OTJk6ivr4eLiwsSEhKQlJSEpKQkJCcnc2ZQH9577z08+eST4s8KhQIKhQIymQxms7lX473uXF1dERMTg4ceegixsbGIiYnhe+AIVlVVhdzcXJw9exaZmZnIyMhAfX09nJycEB8fLwZ0CxYsGAp9DYZWQGA2m1FcXGw3hS0rKwudnZ3iF3LbtKioqChMnTp1uEzVoFHGYDAgOzsbaWlpyMzMRElJCaqqqsQZBzKZzK5L84YNG/Dmm29KVe6o1H16mO00Ly9P3IdTrVbb7UbSfQkLC2OiT3QDqqur7aZi2pYLFy6IM/8CAgLEqZi2Ux6xpf9ZrVacPXtWbLR24sQJdHZ2Ijw8HElJSeLRciIjI/k+NwqVlpYiLS0NOTk5SE9Px9mzZyGTyRAXF4eUlBQsX74cc+bM4awRAPfffz9OnDghdsP39/eHm5sbDAYDKisrUVhYiLq6OjEMSE5OFmdpMGyj6upq8TDmOTk5OH36NCwWC6ZNmyYeUnPu3Ll9HvlhgEkbEBgMBpw4cQJHjx7FqVOncPbsWbS3t8Pd3V3cL8rWxX3cuHFSlUnUb0pLS5GVlYXTp08jPT0dubm5aG9vh5OTExITEzFz5kwkJydjwYIFbN7UTwRBwKVLl5CVlYXs7GxxJlL3ICAqKgpRUVGIjo5GZGQkYmJieIxcokFgm/qcn58vhnQFBQW4ePEiOjs7oVAoEBERgZiYGMTGxorfC4bAFpZhpa2tDd9++y3+8Y9/4NChQ6ivr0dAQACWLFmCJUuWYPHixXzPoz7V1tbi0KFDOHjwIA4dOoTa2lr4+flhyZIlSE1Nxa233irFAGbIEAQBWVlZ+OKLL3DgwAHk5eXBwcFBPAzi4sWLkZiYyKNK0M/S6/U4evQoDh06hEOHDqGkpASurq6YP38+li9fjtTU1ME67OLgBgQtLS1IS0vD0aNHcfToUZw5cwaCICAmJgZJSUniB//kyZP5QqJRwWKxoLCwEFlZWcjKykJ6ejry8/OhUCgQHx+PhQsXYuHChUhKSuLUs+tUWVmJ7OxsMYjJzs6GXq+HUqnE1KlTERcXh8jISERHRyMqKgpBQUFSl0xEPXR1deHixYsoKChAfn6+2LirrKwMABARESF+Z0hMTMT06dP5HtlDe3s7Dhw4gM8++wz79u2DyWTCggULcOutt2LJkiWYOnWq1CXSMCMIAs6fP4/vvvsO3377LX744Qe4ublh5cqVuPvuu7F06VI4OTlJXeaAEwQBmZmZ2L17N3bv3o3y8nJERERg+fLlWLx4MRYsWMBdMuimXb16VQwL9u/fj7a2NiQlJeHOO+9EamrqQPYuG/iAoLi4GHv27MG+fftw+vRpdHV1ITIyUhz4LFiwgNNsiLqpq6vDsWPHxCCtqKgIjo6OmDVrFpYvX4477rgD48ePl7rMIUEQBBQUFODIkSM4duwYMjMzUVNTA7lcjsmTJyMxMREJCQlITExEXFzcqPjiQjSS1dXViYGqbdHpdFAoFIiMjMTcuXPF7xajdRfEU6dO4f3338c//vEPtLa2Yv78+bj77ruxevXqUfuc0MCoqanB7t278dlnn+HkyZPw8PDAnXfeiccffxzx8fFSl9fvLl++jO3bt2PXrl2orKzEpEmTcOedd+LOO+9EXFyc1OXRCNbe3o6DBw9i9+7d+Prrr2E0GjFnzhw8/PDDuOeee/o7IO//gEAQBJw+fRp79uzBnj17UFRUBF9fX6xYsQKLFy9GcnIyD8tBdANqampw5MgRHD58GF9//TXq6+sRFRWFVatWYdWqVYiPjx9V+4kWFRXh6NGjOHbsGI4dOwadTge1Wo358+eLM5Hi4+O5iwbRKFFeXo6srCycOnUKJ06cwP+1d+ZxTV3bHv8lzBAIoMwyyqAiiuIAggoiolWromjr0GevVau1trXa2tf2tu/a1mtHr0O19d5qJ9Fa64BaZ7ROCCKiIAIOgCLzkDCP6/3hPacJCTKUJAT29/M5nyQnO2evs3PW2muvPSUkJKCxsRHe3t4YN24cQkJCMGbMmG69g0JVVRWioqKwbds2JCQkYPDgwVi0aBFmzZqlriGpjB5OTk4O9u3bh3//+99ISUnByJEjsWzZMsyZM0erpyDU19fj8OHD+Oabb3DmzBk4ODjgxRdfRGRkJAYOHKhp8Rg9kNraWpw6dQpRUVHYv38/DA0NMX/+fCxdurRDu2ooofMCBHfu3MG///1vREVF4fHjx3Bzc+MbMKNGjWJTBhiMTqCxsREXLlzAwYMHcejQIWRmZsLR0RFz587FokWL4OHhoWkRO53a2lqcPn0av/32G37//Xfk5ubC1NQUY8aMQXBwMEJCQuDr68tsDIPBAPBkVX5ufaOYmBjcuHEDAODr64tnn30WERERneVEaZyysjJ8/vnn2Lp1K6qrqzFr1iwsX74co0aN0rRojB7M+fPnsW3bNhw4cACmpqZ49dVXsWrVKq0K3FdWVmLLli3YuHEjCgoKMGnSJCxduhTPPPMM8zcYXYaioiLs2rUL3377LTIyMhAUFIT33nsP4eHhf+Wyfy1AUFNTg19//RU7duzAhQsX4OzsjIULF2LGjBlsbhuDoQYSExNx4MAB7Nq1C48ePUJwcDAWL16MiIgIrR5OX1lZid9//x2//fYbjh5lX02QAAAgAElEQVQ9ivLycowcORLPPvssQkJCMGzYMOjq6mpaTAaDoQWUlpbi/PnzOHXqFA4dOoScnBx4eHggIiICM2fOxLBhw7RuFFZVVRU2bdqETz/9FAKBAKtWrcKSJUvYQoOMLkVeXh62b9+OjRs3Qk9PD2vXrsXy5cthZGSkadFapLq6Gtu2bcOGDRtQVVWFFStW4OWXX4azs7OmRWMwWoSIcPbsWXz55Zc4duwYAgMD8Y9//APjxo3ryOW2gjpATk4Ovfnmm2RhYUF6enoUERFBx48fp8bGxo5cjsFg/EUaGhroyJEjNG3aNNLV1aVevXrRmjVrKDc3V9OitZmGhgY6fPgwRUREkJGREeno6FBwcDBt2rSJHj58qGnxGAxGN6CpqYkuX75Mq1evJjc3NwJATk5O9Oabb1JqaqqmxWsT33//Pdna2pJIJKL33nuPysrKNC0Sg/FUiouLae3atWRsbEwODg4UFRWlaZGUsmvXLrKzsyNjY2Nas2YNFRYWalokBqPdXLlyhSZMmEAAKCQkhG7fvt3eS2xpV4AgNzeXli1bRgYGBmRvb0/r16/XqgbIXyEqKooAEAAyMDDQtDhERHTgwAFeJgBUXV2taZE6FRMTE7n7kz0MDAzIx8eHtmzZQk1NTZoWtUuRk5NDH330Edna2pKhoSG9+uqrVFBQoGmxWiQvL4/ef/99sre3J6FQSKGhobRjx44uLbMq6Yq2RpN89tlnfHk4ODhoWpxOg9m3rkFiYiK9//775OrqSgAoKCiIoqKiqL6+XtOiKZCfn08zZswgoVBIy5cvp/z8fE2L1Omo0/5pm23RNnmVkZubS0uWLCGBQEBz5syh4uJiTYtERE/8psmTJ5NQKKRXXnmF8vLyNC2SSmD61Ta0WXZZLly4QCNGjCBDQ0PasGEDNTQ0tPWnbQsQ1NXV0ccff0wikYgcHR1p+/btVFNT03GJtZjQ0NAu57RPmzatWwYIiJ44bwBo2rRp/Lna2lpKTEykwMBAAkBr1qxRWf7l5eXk7u5OkydPVlkeqqK6upq2bNlCDg4OJBaL6dNPP+1STm9mZiYtXryYDA0Nydramt599126d++epsXqMnRFW6NJBg8erNKKWhO6rmn7xviTxsZGOnnyJM2aNYt0dHTIxcWFNm/eTLW1tZoWjYiIjhw5QtbW1uTs7Exnz57VtDgqR532T9W2pbPRNnmVceLECerTpw/Z29vTqVOnNCrLzz//TBYWFuTh4UEXLlzQqCzqQtP6pS2+dXfQtfr6elq/fj0ZGBiQv78/3b17ty0/2yJsbRLCrVu3MGLECHz88cdYu3Yt0tLSsHTpUq2e38zQbvT19eHr64uoqCgIhUJ89dVXKCkp6fD1RCIRgoKClH5HRGhqakJTU1OHr68pDA0N8corryAtLQ2vv/46/v73vyMgIACpqakalUsikWD16tXw8vLC2bNnsXHjRmRmZuKjjz6Cm5ubRmVjdG+0QdfVad8YfyIUChEWFoZ9+/YhPT0dU6ZMwVtvvYX+/ftj7969GpXt66+/xrRp0zBp0iTcvHkTISEhGpWHof1o2i5MmDABt27dwujRozFp0iTs3LlT7TIQEd577z3Mnz8fCxYswI0bN5itVBNdpb5VB5rWNV1dXaxduxYJCQmora2Fv78/Ll++3Orvnhog2L9/PwICAiASiZCUlIR33323Sy8swuhZODo6ws7ODg0NDUhKSlJJHqamprh37x6OHTumkuurAxMTE3z44YdITEyEjo4ORo4cicOHD2tEllOnTsHHxwc//PADPv/8c6SmpmLp0qXMrjA0TlfTdXXYN4Zy3NzcsHnzZqSlpWHMmDGYO3cunn32WeTl5aldlh07dmDFihVYt24ddu3aBTMzM7XLwGCoAnNzc0RFRWHt2rVYtGgRfvrpJ7Xm/7//+7/YsGED/vOf/+Bf//oXjI2N1Zp/T6ar1bc9AW9vb1y8eBGBgYGYOHEi4uLinpq+xQDBTz/9hNmzZ2PevHk4e/Ys3N3dO11YBuOvQv/dhEOb99hVF/369cPFixfxwgsvYMaMGdizZ49a8//Xv/6FiRMnwt/fH7dv38aKFSugp6enVhkYDG2C2TfN4ujoiJ07d+LChQtIS0vDkCFDcO3aNbXlf+nSJSxfvhwffPAB3nnnHbXly2CoC4FAgHXr1uGtt97C3/72N7Xp1/fff48NGzbgu+++w4svvqiWPBkMTWNsbIx9+/Zh7NixmDp16tOD3somHly4cIF0dXVp7dq1nTUFolNpvjjfgwcPaPbs2SQWi8nS0pImT56sdI5FUVERvfHGG+Tm5kZ6enpkbm5OEydOVDqfLzU1laZNm0ZmZmZkbGxMQUFBdOHChRbn7RQUFNCrr75Kzs7OpKenR71796YZM2ZQYmJih+6xpqaG3n//ffLy8iIjIyOysLCgKVOm0KFDhxQWmeDWIGhLOdTX19OePXto/PjxZGNjQ4aGhjRw4EDauHGj3C4Uzcv4zp07FBkZSZaWlvy5t99+W24Rj7i4OBo3bhyJRCIyMjKi4OBgunjx4l8qK2VzdDmysrJIIBCQmZkZSSSSdt+j7CIksoeOjo7SMmi+xkN7nqeuxuuvv076+vp09epVteT37rvvko6ODm3evFkt+XUWPcHWNJdXX1+fHBwcKDQ0lHbu3ElVVVVERLRu3Tq+HAIDA/nf/v777/z5Xr16tVh2mZmZNHv2bBKJRGRpaUnz58+nkpISevDgAU2ZMoVEIhHZ2trSSy+9RFKpVEFGZXMBVanrpaWlCunXrVvH5yt7fubMmR36fzpi39qaR2v3/Ff/T2V1wo4dOzqkL9qARCKh8PBwEolEdP36dZXnV1dXR3379qWpU6dqbKHKnmL/ONuSmppKzzzzDJmZmbXow3QFH6qledFtKffW7IKmaGxspLCwMBowYEB7FlLrEDk5OSQSiTS+vktP06+W7pvzrTtaHu2tD3u6rhERSaVScnd3V+p7/BfFRQrr6+vJw8ODpk6dqlrpOgGuYTxt2jS6fPkyVVRU0KlTp8jIyIiGDx8ulzY3N5dcXV3JxsaGoqOjSSKRUFpaGkVERJBAIKAdO3bwaTMyMsjc3JwcHBzo5MmTVF5eTjdv3qQJEyaQi4uLglI9fvyYnJ2dycbGho4ePUrl5eWUnJxMY8eOJUNDQ7p8+XK77+2ll14isVhMJ0+epKqqKsrLy6PVq1cTAIqJiWm1HM6cOUNmZmYK5RAdHU0A6JNPPqGSkhIqLCykTZs2kVAopNWrV7dYxmPHjqWYmBiqrKyk2NhY0tHR4bd/GTx4MJmYmFBAQACff3x8PA0aNIj09fXp3LlzHS4rZQ50XV0dv4iXvr4+/fDDD3/pHk1MTOQc5JbKQDZA0J7nqSvS1NREYWFh5O3trfLtSQ8fPkwCgYC+++47leajSrqzreHktbW1pejoaJJKpZSXl8c3IL/66iu59C3pi5+fn1yDsnnZRURE0LVr16iiooJ++OEHAkCTJk2iadOmUWJiIpWXl9P27dsJAL3xxhsK11FWUatD1ydOnEhCoVCpYxIQEEC7d+/mP6vDvrU3j9buuaP/59PqhPboizZRX19PEyZMICcnJ6VBrM7kP//5DxkYGFB2drZK82kL3dn+ET2xLWKxmEJCQujixYtUXl7eog+jaR+KS9/cFrbXJ2nNLmiCjIwM0tXVlbOpqmDlypXk5OTUZRZb7wn6payR3dIC6+0pj/bKzHTtT7jOgPj4eGVfKwYIDh06REKhUCtWE+ceoujoaLnzs2bNIgBy+5cuXLiQACjsvVpTU0P29vZkZGTEb2sSGRlJAOjXX3+VS5uTk0MGBgYKSvU///M/BIB+/vlnufO5ublkYGBAfn5+7b43V1dXGjVqlMJ5T0/PFgMEzcth7ty5CuUQHR1NwcHBCtedP38+6enpKfRUcdc+duxYi7IOHjyYAChEGG/evEkAaPDgwfy59pYV50ArO2bMmKHUaW/vPXak0dCe56mrkpqaSgKBgI4fP66yPJqamsjHx4eef/55leWhDrqzreHk3bt3r8J3EydO7LQAwdGjR+XOe3t7EwA6f/683HlXV1fy8vJSuE5LAQJV6/rp06cJAC1fvlwu7cWLF8nJyUluZxB12Lf25qGqAMHT6oT26Iu2UVRURGKxmDZs2KDSfMLDwykyMlKlebSV7mz/iP70Ya5cuSJ3XpkPo2kfikvf3Ba21yfpqo2WZ5999mm9mn+ZxsZGsrGxoU8++URlebSXnqBfHQkQtKU82isz0zV5BgwYQKtWrVL2lWKA4M0336Rhw4apXqpOgHuImjfE3njjDQJASUlJ/DmxWEwAlEb9FyxYQADo+++/JyIiU1NTAkDl5eUKaX18fBSUSiwWk1AoVKgYiIiGDh1KAOjhw4fturdly5YRAFq8eDFduXLlqUOuWiqHNWvWKJRDS3BDYZpH27hrFxUVtfhbLiKnDHt7ewJAjx8/JqL2l5WyHrZHjx7RnDlzCAC99dZbrd5ba/fYkUZDe56nrsygQYPonXfeUdn1s7OzCYDSoVvaRHe2NU+TVxkdbVA237M9LCyMAFBlZaXc+aCgIDI1NVW4Tnu2G+pMXSciGjJkCBkbG8vZwWnTptGXX34pl04d9q29eagqQPC0OqE9+qKNvPzyyzRmzBiV5mFtbd1lpmR1Z/tH9MS2GBoaKp3K0dyHaQl1+VBc+ua2sL0+SVdttHz22Wfk6OiosutzPsmlS5dUlkd76Qn61ZEAQVvLoz0yM12TZ9myZTR27FhlXyluc1hSUgIrK6vmp7s0YrFY7rO+vj4A8Ntn1NbWQiKRwNDQEKampgq/t7GxAQDk5eWhtrYW5eXlMDQ0hEgkUkhrbW0t95m7dlNTE8RiMQQCgdxx/fp1AEBGRka77mnr1q344YcfcP/+fYSGhsLMzAwTJ07EgQMHWvxN83IQCp/8vbLbiEgkEvz973+Hj48PLCwseDnXrFkDAKiqqlJ6bRMTk6fKa25urvQ8V14FBQWdVlYODg7YtWsX+vbti88++0xhUZuO3mNbac/z1NWxsrJCcXGxyq6fn58PALC3t1dZHuqku9ma1uTtTJqvvi4UCqGjo6OwcrSOjk6btz5Sta5zvPnmm6iqqsLXX38NAEhPT8cff/yBl156iU+jDvumqvqmI7RWJwCt64u24uDgwNs2VSGVShXKT9N0N/snS69evSAQCFqUo6CgAIBmfaiW6E4+ibm5OSQSicquL5VKASg+y12B7qxfHaGt5dFemZmu/cnT9E0hQODi4oLU1FR+9eTugIGBAcRiMWpqalBeXq7wPVfR29rawsDAAKampqipqUFFRYVC2ub7URsYGMDc3By6urqor68HESk92rtvsUAgwIIFC3D69GmUlZXh4MGDICJERETgyy+/bNe1ZJk6dSrWrVuHxYsXIz09HU1NTSAifPXVVwDQ4f+9uLhY6W85RbO2tu7UsjI0NMQnn3wCIsLatWv/0j0qcwqeRnuep65MU1MTUlNT4ebmprI8PDw8IBQKW91OpbugbbamNXmVIRQKUVdXp3C+rKyszfl2FqrWdY45c+bA0dERW7ZsQW1tLb744gssXrxYzkFQh33rSB6t3XNX+j+1hdjYWPTr10+ledjZ2SErK0uleXQ22mb/ZGnJSZb1YQDN+lAt0RGfpKO2UNVkZWXBwcFBZde3s7ODQCBAdna2yvJQFdqsX6qgozIzXfuTzMzMFvVNIUAwc+ZMZGZmdru9KWfMmAEAOHr0qNz52tpanDlzBkZGRggPDwcATJo0CQBw/PhxubRFRUVIS0tTuHZERAQaGhpw6dIlhe82bNgAJycnNDQ0tEtec3Nz3LlzBwCgp6eHsLAwHDx4EAKBQOEe2kpjYyMuXboEW1tbrFy5ElZWVvyDW11d3aFrctTU1CA+Pl7u3K1bt/D48WMMHjwYdnZ2ADq3rCIjIzFkyBCcOXMGp06dAtCxezQ2NpZzkL28vPDtt98+Ne/2PE9dlQMHDiA3NxcREREqy0MsFiMyMhLr1q3rtN7cro622RpOXmU2f8iQIXjjjTfkztnZ2SEnJ0fuXF5entodLnXpOgDo6uritddeQ0FBAb744gvs2bMHK1euVEinavvWkTxau+eu8n9qCxcuXMCxY8ewePFileYTEhKC6OholeahCrTN/nFUVFQgKSlJ7lxzH6Yr+FAt0V6fpKO2UJUQEQ4fPqzSRqilpSV8fX2fOhq3K6Ot+qUqOiIz07UnVFVV4cSJExg3bpzyBMomHsyePZucnZ27/EJCLc1f4baOkV2Eovmqk1KpVG7VyW+//ZZPe/fuXbK0tJRb+TMlJYXCw8PJ2tpaYd5Ofn4+9e3bl9zc3OjYsWNUVlZGxcXFtH37djI2Nla6+FdriMViGjt2LCUlJVFNTQ3l5+fThx9+SADoo48+6nA5jBs3jgDQp59+SoWFhVRVVUVnz54lJycnAkCnTp1q07Vl4VYADg0NbXVV0PaW1dO2ASMiOnr0KAGgoUOH8vMH23uPEydOJLFYTNnZ2XT58mXS1dWl27dvP7UM2vM8dUVyc3PJ3t6eXnjhBZXn9eDBA+rduzfNnDmTamtrVZ6fKujOtoaT187Ojo4cOUJSqZQePnxIy5YtIxsbG8rKypJLv2LFCgJAmzdvpvLycrp79y7Nnj2bHBwcnjpnvXnZhYeHK93uZ+zYsUrnCCqbC6gOXeeQSqUkFotJIBC0qDfqsG/tzaO1e+6s/7MtaZTpizaRmppKNjY2NGPGDJVvPXj58mUCoNJFZNtKd7Z/RH/OSw4KCqLY2Nin+jCa9qG49K2trN6aT9KaXdAEBw4cIIFAoPJtRLdv304GBgZdZjH2nqBfHVmDoC3l0V6Zma79yccff0wikYgKCgqUfa24SCHRkz0l3dzcyM/Pj4qLi1UrYQe4cuUKAfIrPr/77rtERArnJ0+ezP+uqKiIXn/9dXJ1dSU9PT0Si8UUHh5OZ86cUcgjLS2Npk+fzu+HO3z4cDpy5AiFhoby1160aBGfvri4mFatWsXviWllZUUTJkxQqCzayo0bN2jp0qXUv39/MjY2JktLS/L396cdO3bwjklHyqGwsJCWLl1Kjo6OpKenRzY2NrRw4UJau3Ytn9bPz0/ptVuIJ/EKdPv2bQoPDydTU1MyMjKisWPHKl2crq1lZWJiopD/nDlzFK4XFBTEfx8YGNjme+S4c+cOjR49mkxMTMjR0ZG2bt1KRIp7sgKgefPm8b9rz/PUlSgsLKTBgweTp6cnlZSUqCXPP/74g8zMzCgkJERhsbquTE+wNcrktbOzo+eee47S09MV0paVldFLL71EdnZ2ZGRkREFBQRQfH09+fn68vG+//XaLZRcfH69wfv369XThwgWF8x988IHSvYS5/0Bdus7RloVfVW3f2pPH0+65M/7P5nVCR/VFGzh16hRZWlpSYGCg0kW/VMGsWbPIyclJY35Yd7d/yvZGDwkJ4fdGV+bDaNKHepotbG+5t2YX1E1+fj7Z2dnRggULVJ5XXV0dDRo0iPz9/TXaadGT9Ev2/lqqbztaHu2RmenaExISEsjAwIDWr1/fUpItAiLlE6bu37+PkJAQ6Ovr49ChQxgwYICyZAwGfH19UVRUhEePHmlaFEYrJCUlYcaMGRAIBIiJiYGTk5Pa8r516xamTZuG8vJybN68Gc8995za8mYwGIyOIJVK8c4772Dbtm147rnn8N1338HQ0FAteRcXF8PPzw8ODg44efJkmxaGZGgfPd2HkkqlGDduHCQSCeLj41tcRK4zSU1NRUBAAMLCwhAVFQVdXV2V58nQPD1d14AniyyPGTMGgwYNwvHjx/kF7ZuxVelZAHBzc0NcXBxsbGzg5+eHzz//HI2NjaqTmMFgqIyGhgasX78eI0eOhLOzM65evarW4AAA+Pj4ICkpCTNnzsTcuXMRGBiIixcvqlUGBoPBaAu1tbXYtGkT+vbti19++QU///wzdu/erbbgAPBkZf0TJ04gIyMDoaGhKCwsVFveDIY6yM3NRXBwMHJzc3HixAm1BAcAoH///oiOjsbvv/+OqVOntnmRXgZDm7ly5QqCgoLg4uKC/fv3txQcAKBkkUJZbGxscO7cOXz44Yd47733MHDgwA4vkMdgMDTD6dOnMXToUPzjH//A//3f/+H06dPo3bu3RmQxNTXF9u3bERcXB0NDQ4wePRpjx45FdHR0t9o5hcFgaCdlZWX45z//CVdXV7z99tt48cUXkZ6ejueff14j8nh5eSE2NhalpaXw8fHRyoULGQxlHD9+HMOGDUN5eTnOnz+v0h2VlDF69GhcunQJycnJGDRoEM6ePavW/BkMddHQ0IANGzYgJCQEQ4cOxcmTJ1vf2rqt8xXS0tIoMjKSnwd5+PDhTpgF0bOAkvlozY8PPvhA02K2mdbm6DA0y4ULF/gFlaZMmUIZGRmaFkmBmJgYeuaZZ0ggEFDfvn3pk08+ocePH2taLK2nu9kaBkPVXL58mV588UUyNjYmsVhMb731Fj169EjTYvGUlZXRkiVLCAAtWLBAbesgaCPaYv96qg9VWVlJK1euJAAUGRmptrWQWiI/P59fYG7JkiVMt1pBW/RLlp6qa0REycnJNGzYMDI0NKR//vOf1NDQ0JafKV+k8Gn88ccfFBYWRgDI19eXtm7dSmVlZe2XmMFgdDolJSW0adMm8vHxIQA0adIkunz5sqbFapXbt2/Ta6+9RpaWlqSrq0vjx4+nr7/+mgULGAyGSmhqaqK4uDhau3YteXp6yvk0EolE0+K1yN69e6lXr17k7OxMO3fubKuzx2BonPr6evr222+pT58+ZGVlRQcOHNC0SHL8+OOPZGFhQU5OTrRjxw6qq6vTtEgMRofJycmhFStWkL6+PgUEBNCdO3fa8/P2Bwg4rl69SgsXLiRjY2MyNjamhQsXKl2xnsFgqJampiY6f/48LViwgAwNDUkkEtGiRYvo2rVrmhat3VRXV9PevXspMjKSRCIRCYVCCgwMpM8//5zu37+vafEYDIYW09DQQOfOnaOVK1fy29K5ubnR6tWrKT4+XtPitZnHjx/T4sWLSVdXl/r160e//PKLyrddZDA6SmNjI/3000/k7u5O+vr6tHz58i67m9Hjx49p6dKlpK+vT25ubrRz506qr6/XtFgMRpvJzc2l1157jQwNDcnR0ZG2bdvWkUByy7sYtBWJRILdu3djx44dSExMhJeXFyIiIjB9+nQMHz4cAoHgr1yewWAooampCVevXsXBgwfx22+/4e7duxg2bBgWL16M559/vvW5RVpATU0NTp48id9++w3R0dEoKSmBj48Pxo0bh5CQEIwZMwYWFhaaFpPBYHRh0tPTERMTg5iYGJw9exaFhYXw9vZGREQEIiIi4Ovrq2kRO0xGRgY+/PBD7NmzB97e3njllVcwb948iEQiTYvGYEAqleLHH3/E1q1bkZ6ejvnz5+ODDz6Aq6urpkVrlczMTHz00Uf4/vvv4ezsjKVLl2LhwoWwsrLStGgMhlKuXbuGb775Brt374a5uTneeecdLF68GAYGBh253Na/HCCQJSEhAbt378bBgwdx//59ODg4YNq0aZg+fTqCg4Ohp6fXWVkxGD2Ouro6xMTE4MCBAzh8+DByc3Ph7u6OGTNmYO7cuVrt6LZGfX09zp07h+PHjyMmJgZJSUkQCATw9fVFSEgIQkJCMHr06G4RGGEwGB3nwYMHfEAgJiYGOTk5EIlEGD16NMaNG4epU6fCy8tL02J2Krdu3cIXX3yBvXv3wsDAAC+88AKWLVuG/v37a1o0Rg/k1q1b2LZtG3766Sc0NjZi7ty5WLVqlVY+j/fu3cPGjRvx448/oqamBjNnzsTSpUsxZswYTYvGYKCiogJRUVH45ptvkJCQAG9vbyxbtgx/+9vfYGRk9Fcu3bkBAlmSkpJw6NAhHDx4EImJiRCLxQgODuadeR8fHza6gMF4Ck1NTbh58ybv6J4/fx7l5eUYOnQopk+fjunTp2PgwIGaFlMjlJSU4Pz583yv4O3bt6Gjo4NBgwZh+PDh/DFgwAC2vzGD0U2RSqVISEhAfHw84uPjERcXh+zsbBgZGSEwMBAhISEIDg7GiBEjeoQdKC4uxs6dO7F9+3bcv38fo0aNwuzZszFr1izY29trWjxGN+bhw4f49ddf8csvvyA2NhZeXl54+eWXsXDhQrVtXahKqqqqsGfPHnzzzTeIi4uDp6cnZs2ahVmzZmHIkCGaFo/Rg6iursaJEyfw66+/Ijo6GnV1dYiMjMSSJUsQFBTUWdmoLkAgS2ZmJo4cOYKzZ8/i/PnzKCkpQe/evfmAQXBwMAYMGKBqMRiMLg0R4fbt2zh79izOnTuH8+fPo7i4GL1798bYsWMxbtw4TJkyBU5OTpoWtctRUFCAc+fOITY2FvHx8UhMTERlZSWMjY0xZMgQPmAwbNgweHh4sOAkg6FlVFdX48aNG4iPj8e1a9cQHx+P9PR0NDU1wcHBAcOGDcPw4cMxevRojBw5sqPDKrsFTU1NOHnyJH7++WccPnwYFRUVCAwM5IMFtra2mhaR0Q3IycnhgwJXrlyBWCzG9OnTMW/ePISGhnbbevbGjRv48ccfsX//fmRlZcHNzQ2zZs3CzJkz2dRqhkqorKzEsWPHsH//fhw9ehRVVVUIDAxEZGQk5s2bB0tLy87OUj0Bgubcv38fp0+f5o/S0lKYmZnBx8cHfn5+8PPzw+jRo7VinhKD0VFyc3Nx7do1JCQkICEhAbGxsSgqKoJIJIK/vz/Gjx+P8ePHY8iQIRAKhZoWV6tobGzEnTt3+LLljpqaGujr68Pd3R3e3t4YMGAA/Pz84O3tDVdXV1axMxgapqGhAdnZ2UhJScHt27eRkpKChIQEpKWlobGxUcFXCAoKUvv+6dpEbW0tTp48iRXFL3wAAA4BSURBVH379uHQoUOQSqUYMGAApk6divHjx2P06NE9OpjCaDsNDQ1ISkpCdHQ0jhw5guvXr8PIyAiTJ0/GggULEB4eDn19fU2LqVZSUlKwb98+REVFIT09HVZWVggODsb48eMxceJE1qHD6DApKSk4cuQITp8+jYsXL6Kurg4BAQGIjIxEZGSkqkeFaSZAIEtjYyOuX7/O9/zFxcUhPT0dRARHR0cMHz4cI0aMwJAhQzBw4EA2TI6hleTk5CA5ORnXr1/nn/OcnBwIhUJ4eXnxz7m/vz98fX2ho6OjaZG7HTU1NUhKSsLNmzeRkpKClJQUJCcnIy8vDwBgbm4Ob29vDBw4EAMHDoSnpyfc3d3h7OzM/g8Go5OprKzE3bt3cffuXaSmpiI5ORkpKSlIS0tDfX09dHV14e7ujoEDB/J66evrC3d3d02LrrVUV1fj9OnTOH78OE6ePIm7d+/C1NQUISEhmDBhAsaMGQNvb28WkGYAeOKfp6Sk4Pz58zh58iRiYmJQWVkJT09PhIeHY+LEiQgNDWUBpv+SmJiI48eP4/Tp07h06RJqa2vh7e2NsLAwjBs3DgEBAejdu7emxWR0QYgId+7cwaVLl3D69GmcOXMGRUVFsLGxwfjx4xEWFoZJkybB2tpaXSJpPkCgDIlEwjei4uLiEB8fj8ePHwMALC0t4ePjA29vb/j4+PDOfHeY48TQfkpKSnhHl2uIJicno7S0FADQp08fPhgwYsQIDBs2DGZmZhqWumdTXFzM/2e3bt3C7du3kZycjJKSEgCAvr4+XFxc4O7urnC4uLiwxVcZjBaQSCS4d+8eHwiQPXJzcwEAAoEArq6u8Pb25uv1AQMGoH///qzhoWLu3buHEydO4MSJE4iJiUF5eTnEYjECAgIwatQoBAYGYsSIEWxXhB5CeXk5YmNjcfnyZVy+fBmxsbGQSqUwMzNDaGgowsPDMWHCBDa6tw1UVVXhjz/+wKlTp3Dq1CkkJyeDiODp6Ql/f38EBAQgICAAAwcOZB0QPRCpVIq4uDhcuXIFV65cQWxsLEpLS2FkZITRo0cjLCwMYWFhGDRokKZGtnbNAIEyioqK5Bpc3CGVSgE8aXj169ePd9w9PDzg4eEBNzc35mQwOpWamhrcu3cPGRkZcg5vamoqH8iS7Y2WDWj16tVLw9Iz2kpxcTH/38r+3/fu3UNhYSEAQFdXF87OznB2doajoyP/yh0uLi4wNjbW8J0wGKohPz8fDx8+xMOHD5GdnY3s7Gz+c3M9cXJygru7O/r27SsXZHNzc4OhoaGG74TR0NCAmzdv8o3DS5cuITs7G7q6uvDx8cHQoUPh6+uLwYMHY/DgwSywreVIJBIkJSXhxo0buHHjBq5fv47k5GQ0NjbCxcUFgYGBCAgIQFBQEGvEdgIlJSWIjY3lAzBxcXEoLy+Hqakp/Pz8MGjQIF63vL29mU3sRhQWFvK6dvPmTSQmJiI1NRWNjY1wdnbGqFGj4O/vD39/fwwZMqSrdDppT4CgJbKysviev/T0dNy9excZGRl874RQKOQdE+7gnHgnJyfY2tqyeccMOZqampCXl8c7u1lZWXxDMSMjA48ePUJTUxMAwMHBAR4eHnB3d4enpycfDHB0dNTwXTBUiUQikQseZGVl8c9KdnY2Kioq+LSWlpa8vXFycoKjoyPs7e1hY2MDe3t7WFlZwdramtkhRpehtrYWBQUFePz4MfLz85GXl4ecnBz+OeeOmpoa/jd2dnZywTE3Nzc+IODq6tpVnB5GO8jJycGlS5dw9epVviFZUlICgUAANzc3DBkyBL6+vujfvz+8vLzg4eHR4+agd3Vqa2uRkZGBtLQ03L59m/8f79+/DwDo1asX/z9yPdtsKq/q4aZvXLlyBQkJCUhKSkJycjKqqqqgq6sLLy8vPmjQr18/eHl5wc3NjelXF6a0tBTp6elIT09HSkoKkpKSkJSUxLdH7ezs+CDQyJEj4e/vDzs7Ow1L3SLaHyBoCdn5jc17enNzc/kGnr6+PhwcHJT2/vXp0wfW1tawtrZm0dNuQkNDAwoLC5Gfn49Hjx7JObuc85uTk4P6+noAgI6ODuzs7ORGpsgGm1jvMEMZpaWlSntVuWcsLy8PtbW1fHpdXV1YW1vD1tYWdnZ2sLa2hoODA6ytrWFnZwdbW1v06tULvXr1gqWlZY/Yso3RuVRVVaG4uBjFxcUoKCjgG/65ubkKwQBueg2HmZkZ+vTpwwe4lNWXbKRezyArK0uu5/nGjRvIyspCU1MTdHR04OLiAk9PT/Tv3x+enp7w9PSEq6sr+vTpw+yWiqivr8ejR4/w4MEDpKenIy0tDXfu3EF6ejqysrLQ2NgIoVAIFxcXPhgwePBg+Pr6ss6MLkRjYyPu3r3LNyyTkpJw69YtPHz4EETE65eHhwe8vLx4/XJ2doaTkxOzwWqgpKQEDx8+xN27d5Gens4H39LT01FUVAQAMDAwQL9+/TBo0CAMGjSI1zcrKysNS98uum+A4GnU19cjJydHzmFv7sCXlZXx6YVCIaytrWFlZQV7e3tYW1vL9f7Z2dmhd+/esLS0hKWlJUxMTDR4dz2PiooKlJSUoLS0FIWFhcjLy+Md3oKCAuTl5fHnCgoKIPvIW1hYKPTuyjq/9vb2rOeLoRJKSkr45zInJweFhYV8Iy0/P59/fps/s8CTKSy9e/fmgwbKjt69e0MsFsPCwgJmZmYwMzNjvQ/dgIqKCkilUkilUkgkEr7R3/woKiqS+1xdXS13HQMDA74ekw1EccEprr6zsbGBkZGRhu6WoQ3U1NTwTnJ6ejru3LnDf5ZIJACeBNsdHBzg5OQEV1dXvlHj7OzMB0O1zIFWGwUFBSgsLMSjR4+QlZXFj1TLzMxEZmYmcnNz0djYCOBJ3eDp6cn3Ont6evKvrAGpnVRVVSEjI4PXL9lDNphrZ2fH+7GcbnH6xdl25gO0TEVFBe93ZWZm8p072dnZvN5xo0OFQiGcnZ3h4eHB6xj33tnZuTss9NozAwRtoaKiAo8ePZJrYBYWFvKOPNfzUlhYiLq6Ornf6uvr88ECS0tLWFhYKHw2NTWFiYkJxGIx/97Y2BgWFhYwMTHpMUpcV1eHyspKlJaWorKyEpWVlaioqEBZWRn/vqSkhD9KS0sV3isrf87htbGxgbW1Ne8E29ra8oayT58+LJjD6PJwo16aN/64BmBJSYnSBqIy025oaMgHC8RiMczNzfnPsoe5uTmMjIz49Pr6+jAzM4OhoSGMjIxgZmYGPT09iMViDZSI9tDQ0IDy8nJUV1ejpqYGEokE9fX1kEql/DmpVMq/SiQSlJWV8QEA2aOsrAwSiYQf/SaLoaEhLC0t5QJEVlZWcqNOmn9nYWGhgRJh9DTy8/Px4MEDvlHLOdoPHjxAdnY2ysvL+bR6enoK9TRXd1taWsLc3BwWFhawsLDg32tb8KqqqgplZWUoLS1FaWkp/76kpAT5+fn8iB5Zv5Mb0Qg8Gc3DNfxcXFz499xnNa5yzugCFBcXIzMzk+/g5PSMO/Lz8+XSW1pawsbGhu/c5EZJW1lZ8TrW/NDGTrLKykqUlZXJHZzOcfqVn5+PwsJCXudkg+j6+vr8qDku2MK954Kc3TzgxgIEnQHnsLfUgFX2nnMaW0JPTw8ikQjm5uYwMTGBgYEBDAwMYGxsDKFQyDvmZmZm0NHR4Z15XV1dmJqa8tcRCAQt7vCgo6PT4kJDUqmUj0g3h1uRv3lazuHlnGIAKCsrAxGhsrISdXV1qKmpQWVlJSQSCcrLy9HQ0NBiGRgbG0MkEikNtLT0nhvJwWD0ZIgIxcXFkEqlKC0tVdrgbK0xWlNT81QbxcEFDUxNTaGvr8/bJu48ALmgJ2ezAPCN1JZsUWuNWGNj46dW0q3ZGC5AKYus/aqtrUVVVRWAJ449Ny1E9rqcjeN+x6VrqTHfHLFYLBe4kR3xYWZmBlNTUz5oIxaL5b4Ti8Xo1asXC3QytJaSkhJ+5BTX6ZKbm4v8/Hy5kYClpaVy07I4DAwM+KCBsbExTE1NoaenxzdsRCIR7x+Zmpry0xyU2Q7Ox5KF811kkbULzYOAnG0oLS3lv+M6QcrKylq8B0tLS7mAiI2NDR8k4To77O3tWWCP0S5qamrw6NEjhcYwN2KxsLCQ/9zct+fg2iLm5uYwMDCAubk5dHR0IBaLoa+vDxMTE17HRCIRH1CQ9QE4ZNsvHLJ1riyydSinh1KpFA0NDSgrK+N/x/kq5eXlvJ7JBtU4uM5bKysr2Nra8oES2fecrtna2naHUQB/BRYg0CRNTU18Q5nrPS8rK0NFRQX/metZr6ura1MDvLnDq8wB5nhaA+BpjresAZD9rCyAwVXInKHgjAk3UsLExIQ3PiKRSO4zW7SNwdA8UqmUr5hle72bn5NIJKirq+NtU2sNas7+AU+mfcku7Ag83XZxtNYIV+agNKe5wy0bVJUNuMo2HmTtI2fjuCCH7CgLbuQF5zxxDpWpqSl/jsFgtA3Z3vfmr6WlpaiqqpJroHN2RVnQTlkniKzN4lAWNJC1C831nvOHLCws+AAF5/Nwox6av2rbKAhG96V5r7ts7zsX4JJIJGhoaODr/MrKSl53ZPWqoqJCoaHeUr2uLPAl27HQPMhnYWHB66FsnatsBAR3sDXD2gULEDAYDAaDwWAwGAwGg8HA1h49foLBYDAYDAaDwWAwGAzGE1iAgMFgMBgMBoPBYDAYDAYLEDAYDAaDwWAwGAwGg8EAdAHs07QQDAaDwWAwGAwGg8FgMDRK4v8DvwWPyuzQewYAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -179,12 +179,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "00b25c642fa74c5eab902ceab69aaa0b",
+       "model_id": "82c116c664634180af16d211f382ba01",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "VBox(children=(Figure(axes=[Axis(label='Price', orientation='vertical', scale=LinearScale(max=28.78, min=-7.95…"
+       "VBox(children=(Figure(axes=[Axis(label='Price', orientation='vertical', scale=LinearScale(max=28.7799999999999…"
       ]
      },
      "metadata": {},
@@ -220,12 +220,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fb69a20850a64b47959ef874fb53da85",
+       "model_id": "c5841ae2258049d3aa706ba3622d98b5",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "VBox(children=(Figure(axes=[Axis(label='Price', orientation='vertical', scale=LinearScale(max=28.78, min=-7.95…"
+       "VBox(children=(Figure(axes=[Axis(label='Price', orientation='vertical', scale=LinearScale(max=28.7799999999999…"
       ]
      },
      "metadata": {},
@@ -252,7 +252,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "06455685c2c24ecd9f03f57f89a0f286",
+       "model_id": "19571440a1564ccd9e167e534fd9704d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -338,7 +338,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/03_simple_dask_example.ipynb b/notebooks/03_simple_dask_example.ipynb
index 674e0f87..c7cccf85 100644
--- a/notebooks/03_simple_dask_example.ipynb
+++ b/notebooks/03_simple_dask_example.ipynb
@@ -24,7 +24,7 @@
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3 style=\"text-align: left;\">Client</h3>\n",
        "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
-       "  <li><b>Scheduler: </b>tcp://127.0.0.1:36927</li>\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:43249</li>\n",
        "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
        "</ul>\n",
        "</td>\n",
@@ -40,7 +40,7 @@
        "</table>"
       ],
       "text/plain": [
-       "<Client: 'tcp://127.0.0.1:36927' processes=4 threads=4, memory=270.39 GB>"
+       "<Client: 'tcp://127.0.0.1:43249' processes=4 threads=4, memory=270.39 GB>"
       ]
      },
      "execution_count": 2,
@@ -118,9 +118,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/rapids/lib/python3.6/site-packages/cudf/io/hdf.py:15: UserWarning: Using CPU via Pandas to read HDF dataset, this may be GPU accelerated in the future\n",
+      "  \"Using CPU via Pandas to read HDF dataset, this may \"\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -131,57 +139,7 @@
       "1872     1990-01-04    1.00    1.00    1.00    1.00      93     0.0\n",
       "1873     1990-01-05    1.00    1.00    1.00    1.00      93     0.0\n",
       "1874     1990-01-08    1.00    1.00    1.00    1.00      93     0.6\n",
-      "1875     1990-01-09    1.00    1.00    1.00    1.00      93     0.0\n",
-      "1876     1990-01-10    1.00    1.00    1.00    1.00      93     0.0\n",
-      "1877     1990-01-11    1.12    1.12    1.12    1.12      93     1.6\n",
-      "1878     1990-01-12    1.12    1.12    1.12    1.12      93     0.0\n",
-      "1879     1990-01-15    1.00    1.00    1.00    1.00      93     1.2\n",
-      "1880     1990-01-16    1.00    1.00    1.00    1.00      93     0.0\n",
-      "1881     1990-01-17    1.00    1.00    1.00    1.00      93     0.0\n",
-      "1882     1990-01-18    1.00    1.00    1.00    1.00      93     0.0\n",
-      "1883     1990-01-19    1.00    1.00    1.00    1.00      93     1.0\n",
-      "1884     1990-01-22    1.00    1.00    1.00    1.00      93     4.4\n",
-      "1885     1990-01-23    1.00    1.00    1.00    1.00      93     0.4\n",
-      "1886     1990-01-24    1.00    1.00    1.00    1.00      93     0.0\n",
-      "1887     1990-01-25    1.00    1.00    1.00    1.00      93     0.0\n",
-      "1888     1990-01-26    1.00    1.00    1.12    1.00      93     1.8\n",
-      "1889     1990-01-29    1.00    1.00    1.00    1.00      93     0.0\n",
-      "1890     1990-01-30    1.00    1.00    1.00    1.00      93     0.0\n",
-      "1891     1990-01-31    1.00    1.00    1.00    1.00      93     0.0\n",
-      "1892     1990-02-01    1.00    1.00    1.00    1.00      93     0.0\n",
-      "1893     1990-02-02    1.00    1.00    1.00    1.00      93     0.0\n",
-      "1894     1990-02-05    1.12    1.12    1.12    1.12      93     2.4\n",
-      "1895     1990-02-06    1.25    1.25    1.25    1.25      93    23.4\n",
-      "1896     1990-02-07    1.25    1.25    1.25    1.25      93     0.0\n",
-      "1897     1990-02-08    1.25    1.25    1.25    1.25      93     0.0\n",
-      "1898     1990-02-09    1.25    1.25    1.25    1.25      93     0.0\n",
-      "1899     1990-02-12    1.25    1.25    1.25    1.25      93     0.0\n",
       "...             ...     ...     ...     ...     ...     ...     ...\n",
-      "15865769 2016-04-11  469.90  467.84  480.00  465.93  869599    26.4\n",
-      "15865770 2016-04-12  468.00  465.60  469.23  460.54  869599    22.7\n",
-      "15865771 2016-04-13  466.03  469.99  474.43  462.00  869599    55.9\n",
-      "15865772 2016-04-14  470.00  464.17  470.00  454.92  869599    48.0\n",
-      "15865773 2016-04-15  465.55  476.37  481.15  451.51  869599    44.7\n",
-      "15865774 2016-04-18  475.00  470.19  480.00  466.30  869599    25.0\n",
-      "15865775 2016-04-19  470.00  475.82  477.74  464.46  869599    33.5\n",
-      "15865776 2016-04-20  473.45  485.24  487.90  473.00  869599    27.4\n",
-      "15865777 2016-04-21  486.90  482.86  490.03  473.18  869599    24.4\n",
-      "15865778 2016-04-22  483.13  489.97  494.00  468.01  869599    41.4\n",
-      "15865779 2016-04-25  488.90  486.53  489.12  483.13  869599    19.9\n",
-      "15865780 2016-04-26  486.97  487.02  502.00  485.01  869599    20.9\n",
-      "15865781 2016-04-27  485.75  487.40  490.43  483.54  869599    24.2\n",
-      "15865782 2016-04-28  489.80  475.56  489.80  472.06  869599    33.4\n",
-      "15865783 2016-04-29  477.39  476.54  489.40  473.42  869599    18.8\n",
-      "15865784 2016-05-02  479.95  486.16  487.91  479.95  869599    22.1\n",
-      "15865785 2016-05-03  482.25  481.27  497.50  478.82  869599    14.4\n",
-      "15865786 2016-05-04  477.60  476.26  481.00  466.00  869599    16.9\n",
-      "15865787 2016-05-05  478.16  481.67  495.21  478.16  869599    22.0\n",
-      "15865788 2016-05-06  479.60  481.30  496.25  479.47  869599    23.8\n",
-      "15865789 2016-05-09  481.02  481.23  484.96  480.41  869599     5.5\n",
-      "15865790 2016-05-10  481.28  485.76  486.90  481.28  869599    18.6\n",
-      "15865791 2016-05-11  483.19  479.70  484.48  475.70  869599    15.1\n",
-      "15865792 2016-05-12  481.65  486.00  488.79  470.41  869599    28.5\n",
-      "15865793 2016-05-13  484.81  481.32  487.77  470.02  869599    24.0\n",
       "15865794 2016-05-16  481.32  481.46  487.45  478.24  869599    28.7\n",
       "15865795 2016-05-17  482.67  484.88  493.07  480.01  869599    36.9\n",
       "15865796 2016-05-18  485.58  483.91  489.04  480.81  869599    20.1\n",
@@ -190,6 +148,14 @@
       "\n",
       "[19277162 rows x 7 columns]\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/rapids/lib/python3.6/site-packages/fsspec/implementations/local.py:33: FutureWarning: The default value of auto_mkdir=True has been deprecated and will be changed to auto_mkdir=False by default in a future release.\n",
+      "  FutureWarning,\n"
+     ]
     }
    ],
    "source": [
@@ -216,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -263,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -284,17 +250,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "id:node_csvdata_dask process time:0.738\n",
-      "id:node_minVolume process time:0.668\n",
-      "id:node_volumeMean process time:0.124\n",
-      "id:node_outputCsv process time:1.708\n"
+      "id:node_csvdata_dask process time:0.041s\n",
+      "id:node_minVolume process time:0.860s\n",
+      "id:node_volumeMean process time:0.110s\n",
+      "id:node_outputCsv process time:1.560s\n"
      ]
     }
    ],
@@ -304,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -359,261 +325,11 @@
        "      <td>18920.967128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1551</td>\n",
-       "      <td>137.250647</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1556</td>\n",
-       "      <td>255.882891</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1562</td>\n",
-       "      <td>185.334286</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1565</td>\n",
-       "      <td>66.781550</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1568</td>\n",
-       "      <td>948.372821</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1570</td>\n",
-       "      <td>2026.146426</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>1571</td>\n",
-       "      <td>105.151335</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>1576</td>\n",
-       "      <td>97.030780</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>1578</td>\n",
-       "      <td>544.907474</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>1580</td>\n",
-       "      <td>1387.531068</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>1581</td>\n",
-       "      <td>311.916046</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>1583</td>\n",
-       "      <td>984.979860</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>1586</td>\n",
-       "      <td>624.883842</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>1587</td>\n",
-       "      <td>66.372422</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>1589</td>\n",
-       "      <td>335.873462</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>1592</td>\n",
-       "      <td>127.409332</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>1595</td>\n",
-       "      <td>523.506921</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1597</td>\n",
-       "      <td>69.508729</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>1598</td>\n",
-       "      <td>5667.614178</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>1609</td>\n",
-       "      <td>128.506439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>1611</td>\n",
-       "      <td>2491.793377</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>1614</td>\n",
-       "      <td>890.201513</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>1619</td>\n",
-       "      <td>390.839224</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>1625</td>\n",
-       "      <td>267.357030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>1626</td>\n",
-       "      <td>52.506585</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3654</th>\n",
-       "      <td>869489</td>\n",
-       "      <td>102.546743</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3655</th>\n",
-       "      <td>869492</td>\n",
-       "      <td>140.843590</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3656</th>\n",
-       "      <td>869497</td>\n",
-       "      <td>57.606204</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3657</th>\n",
-       "      <td>869499</td>\n",
-       "      <td>268.340461</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3658</th>\n",
-       "      <td>869502</td>\n",
-       "      <td>53.870074</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3659</th>\n",
-       "      <td>869504</td>\n",
-       "      <td>74.247626</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3660</th>\n",
-       "      <td>869509</td>\n",
-       "      <td>1699.036940</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3661</th>\n",
-       "      <td>869510</td>\n",
-       "      <td>432.329984</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3662</th>\n",
-       "      <td>869511</td>\n",
-       "      <td>224.161922</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3663</th>\n",
-       "      <td>869517</td>\n",
-       "      <td>181.235935</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3664</th>\n",
-       "      <td>869527</td>\n",
-       "      <td>54.682459</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3665</th>\n",
-       "      <td>869532</td>\n",
-       "      <td>123.373937</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3666</th>\n",
-       "      <td>869533</td>\n",
-       "      <td>8778.535845</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3667</th>\n",
-       "      <td>869535</td>\n",
-       "      <td>191.725729</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3668</th>\n",
-       "      <td>869539</td>\n",
-       "      <td>127.848087</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3669</th>\n",
-       "      <td>869541</td>\n",
-       "      <td>2002.128376</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3670</th>\n",
-       "      <td>869543</td>\n",
-       "      <td>348.918650</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3671</th>\n",
-       "      <td>869544</td>\n",
-       "      <td>137.447304</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3672</th>\n",
-       "      <td>869546</td>\n",
-       "      <td>223.552076</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3673</th>\n",
-       "      <td>869551</td>\n",
-       "      <td>225.113978</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3674</th>\n",
-       "      <td>869554</td>\n",
-       "      <td>867.364437</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3675</th>\n",
-       "      <td>869557</td>\n",
-       "      <td>110.941843</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3676</th>\n",
-       "      <td>869558</td>\n",
-       "      <td>1120.226836</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3677</th>\n",
-       "      <td>869567</td>\n",
-       "      <td>239.959380</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3678</th>\n",
-       "      <td>869571</td>\n",
-       "      <td>658.857428</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>3679</th>\n",
        "      <td>869577</td>\n",
        "      <td>150.850651</td>\n",
@@ -650,57 +366,7 @@
        "2       1404   2073.026646\n",
        "3       1544     80.645555\n",
        "4       1545  18920.967128\n",
-       "5       1551    137.250647\n",
-       "6       1556    255.882891\n",
-       "7       1562    185.334286\n",
-       "8       1565     66.781550\n",
-       "9       1568    948.372821\n",
-       "10      1570   2026.146426\n",
-       "11      1571    105.151335\n",
-       "12      1576     97.030780\n",
-       "13      1578    544.907474\n",
-       "14      1580   1387.531068\n",
-       "15      1581    311.916046\n",
-       "16      1583    984.979860\n",
-       "17      1586    624.883842\n",
-       "18      1587     66.372422\n",
-       "19      1589    335.873462\n",
-       "20      1592    127.409332\n",
-       "21      1595    523.506921\n",
-       "22      1597     69.508729\n",
-       "23      1598   5667.614178\n",
-       "24      1609    128.506439\n",
-       "25      1611   2491.793377\n",
-       "26      1614    890.201513\n",
-       "27      1619    390.839224\n",
-       "28      1625    267.357030\n",
-       "29      1626     52.506585\n",
        "...      ...           ...\n",
-       "3654  869489    102.546743\n",
-       "3655  869492    140.843590\n",
-       "3656  869497     57.606204\n",
-       "3657  869499    268.340461\n",
-       "3658  869502     53.870074\n",
-       "3659  869504     74.247626\n",
-       "3660  869509   1699.036940\n",
-       "3661  869510    432.329984\n",
-       "3662  869511    224.161922\n",
-       "3663  869517    181.235935\n",
-       "3664  869527     54.682459\n",
-       "3665  869532    123.373937\n",
-       "3666  869533   8778.535845\n",
-       "3667  869535    191.725729\n",
-       "3668  869539    127.848087\n",
-       "3669  869541   2002.128376\n",
-       "3670  869543    348.918650\n",
-       "3671  869544    137.447304\n",
-       "3672  869546    223.552076\n",
-       "3673  869551    225.113978\n",
-       "3674  869554    867.364437\n",
-       "3675  869557    110.941843\n",
-       "3676  869558   1120.226836\n",
-       "3677  869567    239.959380\n",
-       "3678  869571    658.857428\n",
        "3679  869577    150.850651\n",
        "3680  869584    673.502241\n",
        "3681  869589    110.377576\n",
@@ -710,7 +376,7 @@
        "[3684 rows x 2 columns]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -743,7 +409,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/04_portfolio_trade.ipynb b/notebooks/04_portfolio_trade.ipynb
index b9bee048..3fc0dca3 100644
--- a/notebooks/04_portfolio_trade.ipynb
+++ b/notebooks/04_portfolio_trade.ipynb
@@ -265,28 +265,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "id:sort process time:0.268\n",
-      "id:add_return process time:1.427\n",
-      "id:add_indicator process time:0.238\n",
-      "id:volume_mean process time:0.062\n",
-      "id:rename_mean_volume process time:0.001\n",
-      "id:left_merge_mean_volume process time:2.680\n",
-      "id:max_returns process time:0.026\n",
-      "id:rename_max_return process time:0.001\n",
-      "id:left_merge_max_return process time:0.039\n",
-      "id:min_returns process time:0.027\n",
-      "id:rename_min_return process time:0.001\n",
-      "id:left_merge_min_return process time:0.055\n",
-      "id:filter_value process time:0.165\n",
-      "id:drop_columns process time:0.036\n",
-      "id:sort_2 process time:0.072\n",
-      "id:exp_strategy process time:0.780\n",
-      "id:backtest process time:0.002\n",
-      "id:portfolio_opt process time:0.020\n",
-      "id:sharpe_ratio process time:0.001\n",
-      "id:cumlative_return process time:0.611\n",
-      "CPU times: user 6.14 s, sys: 2.35 s, total: 8.49 s\n",
-      "Wall time: 8.65 s\n"
+      "id:sort process time:0.144s\n",
+      "id:add_return process time:1.137s\n",
+      "id:add_indicator process time:0.041s\n",
+      "id:volume_mean process time:0.112s\n",
+      "id:rename_mean_volume process time:0.001s\n",
+      "id:left_merge_mean_volume process time:2.703s\n",
+      "id:max_returns process time:0.025s\n",
+      "id:rename_max_return process time:0.001s\n",
+      "id:left_merge_max_return process time:0.027s\n",
+      "id:min_returns process time:0.022s\n",
+      "id:rename_min_return process time:0.001s\n",
+      "id:left_merge_min_return process time:0.041s\n",
+      "id:filter_value process time:0.344s\n",
+      "id:drop_columns process time:0.012s\n",
+      "id:sort_2 process time:0.060s\n",
+      "id:exp_strategy process time:0.940s\n",
+      "id:backtest process time:0.043s\n",
+      "id:portfolio_opt process time:0.040s\n",
+      "id:sharpe_ratio process time:0.001s\n",
+      "id:cumlative_return process time:2.090s\n",
+      "CPU times: user 7.89 s, sys: 2.1 s, total: 9.99 s\n",
+      "Wall time: 10.2 s\n"
      ]
     }
    ],
@@ -351,7 +351,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "85c8cfb7fbe34ff89e057a665ace4384",
+       "model_id": "b1889a24e21e4f3e9ef987deba9dee7a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -402,29 +402,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "id:load_csv_data process time:59.484\n",
-      "id:sort process time:5.271\n",
-      "id:add_return process time:18.387\n",
-      "id:add_indicator process time:5.701\n",
-      "id:volume_mean process time:0.279\n",
-      "id:rename_mean_volume process time:0.001\n",
-      "id:left_merge_mean_volume process time:3.087\n",
-      "id:max_returns process time:0.277\n",
-      "id:rename_max_return process time:0.001\n",
-      "id:left_merge_max_return process time:2.836\n",
-      "id:min_returns process time:0.278\n",
-      "id:rename_min_return process time:0.001\n",
-      "id:left_merge_min_return process time:2.942\n",
-      "id:filter_value process time:0.931\n",
-      "id:drop_columns process time:0.059\n",
-      "id:sort_2 process time:1.032\n",
-      "id:exp_strategy process time:9.772\n",
-      "id:backtest process time:0.103\n",
-      "id:portfolio_opt process time:0.286\n",
-      "id:sharpe_ratio process time:0.001\n",
-      "id:cumlative_return process time:0.057\n",
-      "CPU times: user 1min 44s, sys: 6.73 s, total: 1min 51s\n",
-      "Wall time: 1min 50s\n"
+      "id:load_csv_data process time:88.344s\n",
+      "id:sort process time:5.336s\n",
+      "id:add_return process time:20.408s\n",
+      "id:add_indicator process time:6.722s\n",
+      "id:volume_mean process time:0.347s\n",
+      "id:rename_mean_volume process time:0.002s\n",
+      "id:left_merge_mean_volume process time:4.962s\n",
+      "id:max_returns process time:0.346s\n",
+      "id:rename_max_return process time:0.001s\n",
+      "id:left_merge_max_return process time:4.598s\n",
+      "id:min_returns process time:0.347s\n",
+      "id:rename_min_return process time:0.002s\n",
+      "id:left_merge_min_return process time:4.709s\n",
+      "id:filter_value process time:0.928s\n",
+      "id:drop_columns process time:0.068s\n",
+      "id:sort_2 process time:1.100s\n",
+      "id:exp_strategy process time:11.242s\n",
+      "id:backtest process time:0.025s\n",
+      "id:portfolio_opt process time:0.300s\n",
+      "id:sharpe_ratio process time:0.001s\n",
+      "id:cumlative_return process time:0.077s\n",
+      "CPU times: user 2min 23s, sys: 6.82 s, total: 2min 30s\n",
+      "Wall time: 2min 29s\n"
      ]
     }
    ],
@@ -452,12 +452,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aaae4209c51743bea12b452c48b0505d",
+       "model_id": "4a7bd2cb99cf4984ace62f15e687d992",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale(), side='left'), Axis(l…"
+       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale()), Axis(label='Time', …"
       ]
      },
      "metadata": {},
@@ -512,8 +512,8 @@
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3 style=\"text-align: left;\">Client</h3>\n",
        "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
-       "  <li><b>Scheduler: </b>tcp://127.0.0.1:33545</li>\n",
-       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:34797/status' target='_blank'>http://127.0.0.1:34797/status</a>\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:45865</li>\n",
+       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
        "</ul>\n",
        "</td>\n",
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
@@ -528,7 +528,7 @@
        "</table>"
       ],
       "text/plain": [
-       "<Client: 'tcp://127.0.0.1:33545' processes=4 threads=4, memory=270.39 GB>"
+       "<Client: 'tcp://127.0.0.1:45865' processes=4 threads=4, memory=270.39 GB>"
       ]
      },
      "execution_count": 11,
@@ -602,23 +602,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "id:load_csv_data process time:0.072\n",
-      "id:volume_mean process time:0.149\n",
-      "id:rename_mean_volume process time:0.021\n",
-      "id:left_merge_mean_volume process time:0.334\n",
-      "id:max_returns process time:0.056\n",
-      "id:rename_max_return process time:0.021\n",
-      "id:left_merge_max_return process time:0.079\n",
-      "id:min_returns process time:0.068\n",
-      "id:rename_min_return process time:0.022\n",
-      "id:left_merge_min_return process time:0.080\n",
-      "id:filter_value process time:0.114\n",
-      "id:backtest process time:0.072\n",
-      "id:portfolio_opt process time:0.130\n",
-      "id:sharpe_ratio process time:10.098\n",
-      "id:cumlative_return process time:10.962\n",
-      "CPU times: user 48 s, sys: 1.75 s, total: 49.8 s\n",
-      "Wall time: 2min 25s\n"
+      "id:load_csv_data process time:0.031s\n",
+      "id:volume_mean process time:0.472s\n",
+      "id:rename_mean_volume process time:0.012s\n",
+      "id:left_merge_mean_volume process time:0.159s\n",
+      "id:max_returns process time:0.055s\n",
+      "id:rename_max_return process time:0.012s\n",
+      "id:left_merge_max_return process time:0.026s\n",
+      "id:min_returns process time:0.046s\n",
+      "id:rename_min_return process time:0.013s\n",
+      "id:left_merge_min_return process time:0.025s\n",
+      "id:filter_value process time:0.046s\n",
+      "id:backtest process time:0.037s\n",
+      "id:portfolio_opt process time:0.420s\n",
+      "id:sharpe_ratio process time:8.605s\n",
+      "id:cumlative_return process time:12.172s\n",
+      "CPU times: user 51.5 s, sys: 1.41 s, total: 52.9 s\n",
+      "Wall time: 2min 12s\n"
      ]
     }
    ],
@@ -644,12 +644,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "98089adc55194d2c965725f939079c64",
+       "model_id": "4fa119d3734b4620a80178a2390e53aa",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale(), side='left'), Axis(l…"
+       "Figure(axes=[Axis(label='Cumulative return', orientation='vertical', scale=LinearScale()), Axis(label='Time', …"
       ]
      },
      "metadata": {},
@@ -687,13 +687,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f0c77510aca34d9ba4ad21a108585e1e",
+       "model_id": "68552bff4fda44f4b71dfad32f284236",
        "version_major": 2,
        "version_minor": 0
       },
@@ -767,7 +767,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/05_customize_nodes.ipynb b/notebooks/05_customize_nodes.ipynb
index b773d335..e092c1e2 100644
--- a/notebooks/05_customize_nodes.ipynb
+++ b/notebooks/05_customize_nodes.ipynb
@@ -122,7 +122,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -166,67 +166,17 @@
      "output_type": "stream",
      "text": [
       "            x         y  distance_cudf\n",
-      "0    0.836387  0.957514       1.271368\n",
-      "1    0.294106  0.109366       0.313782\n",
-      "2    0.495487  0.440543       0.663013\n",
-      "3    0.998370  0.553888       1.141724\n",
-      "4    0.691739  0.253014       0.736559\n",
-      "5    0.000852  0.061719       0.061725\n",
-      "6    0.765695  0.470009       0.898441\n",
-      "7    0.170214  0.214503       0.273833\n",
-      "8    0.567862  0.188615       0.598367\n",
-      "9    0.128882  0.536904       0.552157\n",
-      "10   0.752010  0.497383       0.901615\n",
-      "11   0.027600  0.129488       0.132397\n",
-      "12   0.714133  0.242321       0.754126\n",
-      "13   0.051390  0.874764       0.876272\n",
-      "14   0.424406  0.113438       0.439304\n",
-      "15   0.587216  0.961709       1.126812\n",
-      "16   0.478085  0.133467       0.496366\n",
-      "17   0.363664  0.157096       0.396145\n",
-      "18   0.624918  0.109936       0.634514\n",
-      "19   0.581437  0.400406       0.705970\n",
-      "20   0.961781  0.454890       1.063931\n",
-      "21   0.907983  0.740077       1.171387\n",
-      "22   0.408558  0.849442       0.942587\n",
-      "23   0.949032  0.475539       1.061508\n",
-      "24   0.212582  0.999529       1.021885\n",
-      "25   0.838425  0.166519       0.854801\n",
-      "26   0.564560  0.166126       0.588494\n",
-      "27   0.768162  0.282583       0.818490\n",
-      "28   0.758121  0.827895       1.122567\n",
-      "29   0.925646  0.353130       0.990718\n",
+      "0    0.880657  0.915653       1.270424\n",
+      "1    0.313161  0.803863       0.862708\n",
+      "2    0.715800  0.832247       1.097728\n",
+      "3    0.909188  0.575765       1.076164\n",
+      "4    0.293410  0.937092       0.981952\n",
       "..        ...       ...            ...\n",
-      "970  0.125576  0.018615       0.126948\n",
-      "971  0.826058  0.344783       0.895124\n",
-      "972  0.759709  0.774125       1.084632\n",
-      "973  0.579127  0.659230       0.877481\n",
-      "974  0.446206  0.001899       0.446210\n",
-      "975  0.470431  0.918708       1.032148\n",
-      "976  0.615124  0.183615       0.641944\n",
-      "977  0.925549  0.488118       1.046375\n",
-      "978  0.578353  0.889417       1.060922\n",
-      "979  0.216710  0.790887       0.820040\n",
-      "980  0.219122  0.240300       0.325206\n",
-      "981  0.539036  0.036415       0.540265\n",
-      "982  0.111227  0.477055       0.489850\n",
-      "983  0.927727  0.835949       1.248795\n",
-      "984  0.336382  0.859899       0.923352\n",
-      "985  0.969154  0.858187       1.294505\n",
-      "986  0.281598  0.562810       0.629327\n",
-      "987  0.934636  0.939830       1.325453\n",
-      "988  0.438150  0.028522       0.439077\n",
-      "989  0.549242  0.387627       0.672251\n",
-      "990  0.543592  0.493093       0.733916\n",
-      "991  0.599601  0.560348       0.820678\n",
-      "992  0.683408  0.402713       0.793236\n",
-      "993  0.366721  0.283478       0.463513\n",
-      "994  0.054395  0.082871       0.099128\n",
-      "995  0.345603  0.094344       0.358248\n",
-      "996  0.902845  0.954603       1.313924\n",
-      "997  0.411790  0.047897       0.414566\n",
-      "998  0.507562  0.226668       0.555875\n",
-      "999  0.575396  0.145080       0.593404\n",
+      "995  0.877839  0.285406       0.923070\n",
+      "996  0.320840  0.905872       0.961011\n",
+      "997  0.941912  0.342269       1.002171\n",
+      "998  0.435483  0.489932       0.655499\n",
+      "999  0.970076  0.564717       1.122476\n",
       "\n",
       "[1000 rows x 3 columns]\n"
      ]
@@ -271,7 +221,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A cuDF series can be converted to GPU arrays compatible with the Numba library via `to_gpu_array` API. The next step is to define a Node that calls this Numba kernel to compute the distance and save the result into `distance_numba` column in the output dataframe."
+    "A cuDF series can be converted to GPU arrays compatible with the Numba library via.to_gpu_array` API. The next step is to define a Node that calls this Numba kernel to compute the distance and save the result into `distance_numba` column in the output dataframe."
    ]
   },
   {
@@ -280,6 +230,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import rmm\n",
     "class NumbaDistanceNode(Node):\n",
     "\n",
     "    def columns_setup(self,):\n",
@@ -294,14 +245,14 @@
     "        number_of_blocks = ((len(df) - 1)//number_of_threads) + 1\n",
     "        # Inits device array by setting 0 for each index.\n",
     "        # df['distance_numba'] = 0.0\n",
-    "        # darr = rmm.device_array(len(df))\n",
-    "        darr = cuda.device_array(len(df))\n",
+    "        darr = rmm.device_array(len(df))\n",
+    "        #darr = cuda.device_array(len(df))\n",
     "        distance_kernel[(number_of_blocks,), (number_of_threads,)](\n",
-    "            df['x'].to_gpu_array(),\n",
-    "            df['y'].to_gpu_array(),\n",
+    "            df['x'],\n",
+    "            df['y'],\n",
     "            darr,\n",
     "            len(df))\n",
-    "        # df['distance_numba'].to_gpu_array()\n",
+    "        # df['distance_numba'.to_gpu_array()\n",
     "        df['distance_numba'] = darr\n",
     "        return df"
    ]
@@ -321,16 +272,16 @@
     "\n",
     "CuPy is an alternative to Numba. Numba JIT compiles Python code into GPU device code at runtime. There are some limitations in how Numba can be used as well as JIT compilation latency overhead. When a Python process calls a Numba GPU kernel for the first time Numba has to compile the Python code, and each time a new Python process is started the GPU kernel has to be recompiled. If advanced features of CUDA are needed and latency is important, CuPy is an alternative library that can be used to compile C/C++ CUDA code. CuPy caches the GPU device code on disk (default location `$(HOME)/.cupy/kernel_cache` which can be changed via `CUPY_CACHE_DIR` environment variable) thus eliminating compilation latency for subsequent Python processes.\n",
     "\n",
-    "`CuPy` GPU kernel is esentially a C/C++ GPU kernel. Below we define the `compute_distance` kernel using `CuPy`:"
+    "`CuPy` GPU kernel is esentially a C/C++ GPU kernel. Below we define the `compute_distance` kernel string."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "raw_kernel = cupy.RawKernel(r'''\n",
+    "kernel_string = r'''\n",
     "    extern \"C\" __global__\n",
     "    void compute_distance(const double* x, const double* y,\n",
     "            double* distance, int arr_len) {\n",
@@ -339,7 +290,7 @@
     "        distance[tid] = sqrt(x[tid]*x[tid] + y[tid]*y[tid]);\n",
     "        }\n",
     "    }\n",
-    "''', 'compute_distance')"
+    "'''"
    ]
   },
   {
@@ -351,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -362,19 +313,22 @@
     "                         'y': 'float64'}\n",
     "        self.addition = {'distance_cupy': 'float64'}\n",
     "        self.delayed_process = True\n",
-    "\n",
+    "        \n",
+    "    def get_kernel(self):\n",
+    "        raw_kernel = cupy.RawKernel(kernel_string, 'compute_distance')\n",
+    "        return raw_kernel\n",
+    "        \n",
     "    def process(self, inputs):\n",
     "        df = inputs[0]\n",
-    "        # cupy_x = cupy.asarray(df['x'].to_gpu_array())\n",
-    "        # cupy_y = cupy.asarray(df['y'].to_gpu_array())\n",
     "        cupy_x = cupy.asarray(df['x'])\n",
     "        cupy_y = cupy.asarray(df['y'])\n",
     "        number_of_threads = 16\n",
     "        number_of_blocks = (len(df) - 1)//number_of_threads + 1\n",
     "        dis = cupy.ndarray(len(df), dtype=cupy.float64)\n",
-    "        raw_kernel((number_of_blocks,), (number_of_threads,),\n",
+    "        self.get_kernel()((number_of_blocks,), (number_of_threads,),\n",
     "                   (cupy_x, cupy_y, dis, len(df)))\n",
     "        df['distance_cupy'] = dis\n",
+    "        #df['distance_cupy'] = 0.0\n",
     "        return df"
    ]
   },
@@ -396,12 +350,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -441,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -457,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -489,9 +443,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/rapids/lib/python3.6/site-packages/distributed/dashboard/core.py:79: UserWarning: \n",
+      "Port 8787 is already in use. \n",
+      "Perhaps you already have a cluster running?\n",
+      "Hosting the diagnostics dashboard on a random port instead.\n",
+      "  warnings.warn(\"\\n\" + msg)\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -500,26 +465,26 @@
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3 style=\"text-align: left;\">Client</h3>\n",
        "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
-       "  <li><b>Scheduler: </b>tcp://127.0.0.1:45147</li>\n",
-       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:33519</li>\n",
+       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:35345/status' target='_blank'>http://127.0.0.1:35345/status</a>\n",
        "</ul>\n",
        "</td>\n",
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3 style=\"text-align: left;\">Cluster</h3>\n",
        "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
-       "  <li><b>Workers: </b>8</li>\n",
-       "  <li><b>Cores: </b>8</li>\n",
-       "  <li><b>Memory: </b>540.95 GB</li>\n",
+       "  <li><b>Workers: </b>4</li>\n",
+       "  <li><b>Cores: </b>4</li>\n",
+       "  <li><b>Memory: </b>270.39 GB</li>\n",
        "</ul>\n",
        "</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "text/plain": [
-       "<Client: 'tcp://127.0.0.1:45147' processes=8 threads=8, memory=540.95 GB>"
+       "<Client: 'tcp://127.0.0.1:33519' processes=4 threads=4, memory=270.39 GB>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -541,7 +506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -562,7 +527,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -592,7 +557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -617,12 +582,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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z53DkyBGcPHkSzc3NmDVrFp599lksWbIE6urqfJdICGmFwishhBAOYwwxMTHYv38/fv31V9TV1cHAwACzZs1CQEAAZsyYgVGjRvFd5hOpq6vDtWvXcO7cOZw9exaJiYkQCARwcHDAyy+/jODgYBgYGPBdJiGkE8p8F0AIIURxCAQCVFVVISwsDAYGBti3bx/EYjHOnj2Ll19+GfX19TAyMoKXlxe8vb3h5eUFV1dXhb1wqbm5Gbdv38b169cRGxuL2NhYpKamQiqVYsyYMZg9ezbef/99nDp1Crt370ZkZCSWLVvGd9mEkC7QkVdCCCEAHl7I9e677+Lbb7/FkiVLsHPnTujr63PjGxoacP36dcTFxSEmJgaxsbHIzc0FAIwcORJOTk6wt7eHg4MDrK2tYW1tDUtLS+jp6fVr3c3NzcjLy4NYLMaDBw9w9+5dZGRkICMjA3fu3EFzczPU1NQwceJELnRPnjwZVlZWbeZz9uxZrF69GsrKyti/fz9mzJjRr3UTQnqHwishhBAkJCRgxYoVKCwsxHfffdftvkvz8/ORnp7OhcWMjAxkZmaitLSUm0ZLSwtWVlYwMDCAvr4+9PX1oaenB319faiqqkIkEgF4+Mtg8sdNTU2oq6sDADQ2NkIikaCmpgYVFRUoLy9HeXk5KioqkJ+fj4KCAshkMgCAqqoqbGxs4OTkhPHjx3P3jo6OUFVVfezrKSkpwbp163Ds2DG8/PLL+Pzzz6GmptajdUkI6V8UXgkhZBhraWnB9u3bsWXLFvj6+mL//v2wsLB44vlKJBJkZ2cjJycHYrEYYrGYC5yt76VSKSoqKrhaqqurATwMoUKhEACgoaEBdXV1aGtrc6FXfm9iYgJLS0tYWlrC2toaJiYmEAgET1x/SEgINm3aBCsrKxw8eJC6xyJEgVB4JYSQYerBgwdYuXIl4uPjsXXrVrz11ltDuleBnnrw4AGef/55xMXF4cMPP6T1Q4iCoHchIYQMQyEhIXB2dkZ5eTliYmLwzjvvUDB7hI2NDS5cuIAPP/wQW7ZsQUBAAMRiMd9lETLsUUtFCCHDSElJCRYtWoRVq1bhhRdewPXr1+kr8S6MGDEC77zzDhISElBSUgJnZ2ccOHCA77IIGdYovBJCyDBx5swZuLm54caNG7hw4QK++eYbuhipm5ydnXHt2jVs2LABq1atwrJly1BeXs53WYQMSxReCSFkiKuvr8fmzZsxd+5cTJ06FUlJSZg+fTrfZQ066urq2LZtG06fPo2rV6/C0dERp06d4rssQoYdCq+EEDKExcfHw83NDT///DMOHDiAP/74o9/7XR3qAgICkJaWhpkzZ2LevHlYv349JBIJ32URMmxQeCWEkCFIKpXis88+w9SpU2FlZYW0tDQ8++yzfJc1ZOjq6uKXX37BoUOHcPjwYXh6eiIxMZHvsggZFii8EkLIEHP//n3MmDEDW7duxUcffYQzZ870Sd+tpL2lS5ciMTERhoaG8PLywtatW9HS0sJ3WYQMaRReCSFkCAkJCYGLiwsqKysRFxdHXWANAGtra1y4cAFffPEFPv30U0ybNg1ZWVl8l0XIkEUtGiGEDAHFxcVYuHBhmy6wXFxc+C5r2FBSUsLmzZuRkJCA2tpaTJw4Ebt37+a7LEKGJAqvhBAyyJ0+fRpubm5ISkrCxYsX8c0330BVVZXvsoYlZ2dnxMXFYePGjdi4cSOWLl2KsrIyvssiZEih8EoIIYOUvAusp556Cj4+PkhMTISfnx/fZQ178i61zpw5g9jYWDg5OSE8PJzvsggZMii8EkLIIHTt2jW4ubkhJCQEBw8epC6wFNCsWbOQmpoKf39/zJ8/n7rUIqSPUHglhJBBRN4Flo+PD6ytrZGWloa///3vfJdFOqGrq4uDBw9yXWp5eHjgxo0bfJdFyKBG4ZUQQgaJe/fuYfr06VwXWKdPn4a5uTnfZZFuWLp0KZKSkmBsbAxvb2/qUouQJ0DhlRBCBoGQkBC4urqiurqausAapKysrLgutbZt2wZfX1/qUouQXqCWjxBCFFhxcTEWLFiA1atX44UXXkBCQgJ1gTWICQQCrkstiURCXWoR0gsUXgkhREGdOnUKbm5uSE1NpS6whhgnJyfExsZyXWotWbKEutQipJsovBJCiIKRSCTtusCaNm0a32WRPibvUuvs2bOIi4uDo6MjTp48yXdZhCg8Cq+EEKJA4uLi4Obmhl9//RVHjhzBH3/8AV1dXb7LIv3I398faWlpCAgIwNNPP43169ejrq6O77IIUVgUXgkhRAFIpVJs3boVU6dOha2tLZKTk7Fo0SK+yyIDRCQS4cCBAzh06BD+/PNPeHp64vr163yXRYhCovBKCCE8y8zMxOTJk/H555/jyy+/xOnTp2FmZsZ3WYQHS5cuRWJiIkxMTKhLLUI6QeGVEEJ4whjD7t274eHhAYFAgMTERGzevBkCgYDv0giPrKysEBERge3bt2Pbtm3w8fHB3bt3+S6LEIVB4ZUQQnhQVFSEBQsW4KWXXsKmTZsQFRWFcePG8V0WURDyLrWuX7+OhoYG6lKLkFYovBJCyAA7cuQInJyckJaWhosXL2Lbtm3UBRbpkKOjI2JjY/HSSy9h48aNWLx4MUpLS/kuixBeUXglhJABUlNTg/Xr12Px4sWYO3cuUlNT4evry3dZRMGpqalh27ZtuHLlCpKSkuDk5ISwsDC+yyKENxReCSFkAMTGxmLixIk4evQojh49ipCQEGhpafFdFhlEpkyZghs3biAoKAhBQUHUpRYZtii8EkJIP5J3geXj44PRo0cjKSkJCxcu5LssMkiJRCLs2rWL61LLxcUFV69e5bssQgYUhVdCCOknN2/ehLe3N9cF1qlTp6gLLNInli5divT0dIwbNw5+fn5499130dzczHdZhAwICq+EENLH5F1geXp6QklJCUlJSdQFFulzJiYmOHnyJHbs2IFvv/0Wvr6+uHPnDt9lEdLvKLwSQkgfKioqwtNPP41//OMf2LRpE6Kjo2FnZ8d3WWSIEggEWLduHeLj49HU1AQ3Nzd88803fJdFSL+i8EoIIX3kr7/+gqOjIzIyMrgusFRUVPguiwwDDg4OiIuLw1tvvYU33ngDc+fORUFBAd9lEdIvKLwSQsgTqq6uxvr167FkyRI89dRTSElJgY+PD99lkWFGRUUFW7duRWRkJO7cuQM3NzccP36c77II6XMUXgkh5AnExMRg4sSJCA0NxbFjx6gLLMI7eZdaCxcuRFBQEFauXIna2lq+yyKkz1B4JYSQXmhubsbWrVvh6+uLsWPHIikpCQsWLOC7LEIAADo6Oti1axcOHz6M8PBwuLi4IDo6mu+yCOkTFF4JIeQRYWFhYIx1Oj4jI6NNF1jh4eEwNTUdwAoJ6Z4lS5YgLS0N9vb2mD59+mO71Lp27RoKCwsHsEJCeo7CKyGEtBIdHY1FixZhx44d7ca17gJLWVmZusAig4KJiQlOnDjBdanl4+OD27dvt5uupqYGS5cuxXPPPdflzhshfKPwSggh/6eyshLLly+HTCbDm2++iczMTG5cYWEh5s+fj3/84x94+eWXERUVRV1gkUFD3qVWQkICpFIp16VW65C6efNm5Ofn48KFC/j66695rJaQrgkY7V4RQggA4JlnnsFff/0FqVQKFRUVjB8/HgkJCTh+/Dg2bNgAkUiEkJAQTJ06le9SCek1qVSKjz/+GB9//DFmzZqFn376CfHx8W1+tlhFRQVxcXGYMGECj5US0jEKr4QQAmDfvn144YUX2gwbMWIEJk2ahNjYWGzYsAHbt2+HpqYmTxUS0reuXLnC9UQglUpRXV0NmUwGAFBWVoa5uTlSU1Ohra3Nc6WEtEXhlRAy7GVlZcHZ2Rn19fXtxgkEAnz66ad45513eKiMkP5VVVUFb29vZGVltbuQS1lZGc8//zz27t3LU3WEdIzOeSWEDGvNzc1YtmwZpFJph+OVlJTw3Xffobq6eoArI6T//frrr7h161aHPRBIpVL8+OOPOHToEA+VEdI5Cq+EkGHtvffeQ3JycqfdB7W0tKCoqAibN28e4MoI6V9ZWVl44403uuxZQCAQYO3atcjOzh7AygjpGp02QAgZts6fP4/Zs2d3u1ugI0eOYNGiRf1cFSH9TyqVYtKkSUhJSUFLS0uX06qoqMDT0xORkZEYMWLEAFVISOfoyCshZFgqLS3F3//+dygpdd0MqqqqAgA0NTVx6tSpgSiNkH4XHx+PhoYGtLS0YMSIEV2G0ubmZsTGxuLTTz8dwAoJ6RwdeSWEDEsLFizA6dOn250uMGLECDDGIJPJYGVlhYULF+Lpp5/GtGnTuCBLyFBRXFyMy5cv4/jx4zh+/Diqq6uhoqLS4Wk0SkpKiIqKwuTJk3molJD/ovBKCOlSTU0NpFIpGhsbIZFIwBhDZWUlN14ikaCxsbHD58pkMlRVVXU6b4FAAF1d3U7Ha2pqQk1NDcDDD06RSAQA0NbWhrKyMtTU1HrVddWOHTuwadMm7m/5h7VIJMKcOXPw1FNPYfbs2TAxMenxvAkZrFpaWhAXF4fTp0/jxIkTSEpKgkAggLKyMpqamqCkpAQzMzOkp6dDR0enV/OXX/hYWVkJxhhqa2u5oCxvYzoilUpRU1PT6bxbtxWPUldXh4aGBoCHO6fy2nV1dSEQCKClpQUVFZUevx7CHwqvhAwRdXV1KC8vR0VFBcrLy1FTU4O6ujpUV1dzjyUSCSoqKrjHNTU1qKqqgkQiQX19PRoaGlBfX//Y0KloWodg+QeRlpYWNDU1oaWlBV1dXWhqakIoFKKxsREhISGQSqUQCAQYNWoUJk+eDF9fX0yePBkGBgbQ19eHuro6z6+KEH7dvn0bJ0+exLlz5xAdHc0Fz4kTJ2LJkiWoqqpCdXU1amtrubamqqoKdXV1aGho4HZsuwqlikQecpWVlaGtrc21I3p6ehAKhdDS0oJQKISenh60tLS4v3V1daGtrQ0dHR3o6+tzbQidH9x/KLwSooCqqqpQUFCAkpISFBcXo6CgAOXl5W3C6aP3TU1N7eYjP8qgra3NhTddXV0IhUJoamq2GydvrIH/HpUQCoVQVVXtcByANsM7oqOj02kj3tTUhLq6ui7Xg7zT9NZHXuTD5YG79REd+bjq6mpIJBIusEskEtTW1iIxMRHAwz4sZTJZm6PIrWloaEBfXx96enod3o8cORImJiYwNDSEsbExTExMIBQKO30thPCpvr4ehYWFKCgoQHFxMfLz81FaWsq1K2VlZe0edxQP1NTUwBiDvr4+zM3Noa2tzYU4kUjE/a2hodEuDAKAnp4egP+2C62PmD7umxj5czvSuq14lPzbI+Dh+bu1tbVtvkGqrq5GS0sLF7bl7VJjYyPq6upQWVmJ2tpaLqR39HdHRCIRDAwMuDArv5c/NjU1hYmJCYyMjGBubt5lO0raovBKyAAqLCyEWCxGbm4ucnJyuGBaUlKCoqIiFBYWori4uM3X8AKBAIaGhjAwMOg0SHU0TEdHp9Ov0Yar5ubmDr8elEgkqK6u7nLn4NFhJSUlqKioaDMfTU1NmJiYwNjYGIaGhtxjExMTWFpawsrKChYWFjAwMBiol0yGOKlUiry8PGRnZyM7OxtisZhrS1oH1Ue/ch85ciRGjhzZLlC1ftw6eIlEIu60HaDz99JwVVtbi6qqqnY7AfLbozsGZWVlKC4ubhO45e2HqakpjIyMYGZmBiMjI67tkN+oXafwSkifaWhoQFZWFu7fv4/c3FyIxWKIxWLk5OQgNzcXubm5bUKpfI/bzMwMhoaGMDIy4hotQ0NDbrihoSGUlZV5fGWkM42NjSgpKUFhYSGKiopQUlLCBYbi4mJueGFhIcrKyrjnaWpqwtraGhYWFrCwsOA+lCwsLGBrawsbGxsKBgTAw5B4//59ZGVlITs7Gzk5OcjJyeHCan5+PtfVlZqaGszNzduFH/kRPmNjY24YXXzIv5aWFhQXF6OoqAj5+R/kR2oAACAASURBVPncjsajOx85OTltfv3P1NQU1tbWbQKtjY0NbGxsMHbs2GFxyhOFV0J6oKGhAfn5+UhPT0dGRgbu3bvH3R48eMDtRaurq8PMzAyjRo2Cqakp91j+t7W1NX1FNMw0NjYiLy8P+fn5KCgo4LYb+d9ZWVltvn40NTWFo6Mjt93Ib+PGjaNtZwiqqKjosF1JT09HQ0MDgLbtSuv2RD7M2tqazrMcoioqKtq1Ha3bj9afP3p6enBwcGjXfjg4OHAXrg12FF4J6UBZWRlSU1ORkZHB3d+8eRMlJSUAHp5LamlpiTFjxmD06NFt7keNGkXnPpJeqaiowL1793D37l1kZWW1uc/Pzwfw8DQSc3Nz2Nvbw8nJCQ4ODnB2doaDgwN3XiFRXGKxGKmpqUhJSUFKSgrS0tJw+/Zt7lsZAwMDjB07FmPHjoWdnR33eMyYMb26wp8MD/X19cjKysKdO3fa3G7fvo2CggIAD8/zt7W1hYuLC5ydneHs7AwXFxeMGjXqsf1dKxoKr2RYa2xsRHJyMpKTk5Geno60tDSkp6ejsLAQwMMLk5ycnODo6Ah7e3uMHTsWo0ePhq2tLX3tRgaURCJpE2YzMjKQlpaGjIwM7kpuGxsbODg4cNvshAkT4ODgQEfjeNDc3Izk5GQkJiYiJSWFC6zy86Stra25AGFvb88FVX19fZ4rJ0NNTU0NF2Zv3bqF1NRUJCcnIysrCzKZDEKhEE5OTlyodXNzg7u7e6+6IRwoFF7JsCGVSnHr1i1cv369za2hoQGqqqoYM2YM3N3d4ejoyH3lYmtry11VT4iiys/PR0ZGBve1c3p6OhITEyGRSKCiogIXFxdMnToV7u7ucHd3h729/aA70qLo8vPzuTYlOjoa0dHRqK+vh7a2Nuzs7ODg4MC1L66urjA0NOS7ZDLMNTU14c6dO7h+/TrXbiQkJKCwsBAjRozAuHHjuDbDx8cHEyZMUJh2g8IrGbLKysoQGRmJy5cv49q1a0hKSkJ9fT2EQiHc3Nzg6ekJDw8PeHh4wM7OjkIqGVKkUin3YRQfH4+EhASkpKRwP8bg7u6OyZMnw8/PD1OmTKFTXXpAJpMhKSkJERERuHLlCuLi4lBcXAxlZWW4uLjAy8uLu40bN47aFjKoZGdnIy4ujrvduHED9fX1EIlE8PT0xNSpU+Hv7w9vb2/eLiyl8EqGjPLyckRGRuLSpUu4ePEi0tLSAAAuLi6YMmUKF1Tpa1QyXMlPk0lISEBCQgKuXr2KW7duQUVFBZ6enpg+fTqmT59OYbYDd+7cQUREBCIiInDx4kWUlZXByMgIfn5+8Pb2xqRJk+Du7j5kLoghRK65uRkpKSlcmI2MjMSDBw8gFAoxbdo0+Pv7w9/fHy4uLgN2ZJbCKxm0ZDIZ4uPjcezYMZw6dQopKSkAAFdXV/j5+WHGjBnw9fXtsmNrQoa7/Px8XLp0CZcuXcLly5dx+/ZtqKioYNKkSZg3bx6CgoLg4ODAd5kDrrm5GRcvXsSRI0dw6tQp5OTkQEtLC35+ftyHtbOzMx1VJcNSVlYWtzN34cIFlJaWwtDQEAEBAQgODsbcuXP79ZxZCq9kUGloaMD58+dx/PhxhIWFobCwEKNGjcL8+fPh7+9PYZWQJ5Sfn4+LFy/iwoULOHHiBIqLizFmzBgEBQVhwYIFmDp16pD95qKhoQFnz57FX3/9hbCwMFRUVGDChAkICgqCv78/vLy8qP9dQh4hk8mQkpKCiIgIhIWFISoqCmpqaggMDERwcDDmz5/f5S+n9QaFV6LwZDIZzp07h59++gknT56ERCKBp6cn92Hq5OTEd4mEDEkymQyxsbE4fvw4jh07hszMTIwcORKLFy/GCy+8gEmTJvFdYp+IiorCrl27EBoaColEAm9vbwQHByM4OBi2trZ8l0fIoFJcXIzQ0FAcOXIEFy5cgEAgwOzZs7F+/XrMnTu3T3Z+KbwShZWdnY19+/Zh3759EIvF8PHxwYoVK/D000/D1NSU7/IIGXbu3LmD0NBQhISEIC0tDc7OzlizZg1WrFgx6H7ytrq6GgcPHsTOnTuRmpoKd3d3rF69GosWLYKZmRnf5REyJFRUVCAsLAwHDhxAREQELC0tsW7dOqxZswYmJia9ni+FV6Jwzp49i6+++grnzp2DkZERVq5ciTVr1sDOzo7v0ggh/ycuLg4//fQTfv/9dzQ2NmLRokV46623MHHiRL5L61J2djY+++wzHDhwADKZDM888ww2bNgAT09PvksjZEi7ffs2du/ejf3796O6uhrBwcH44IMPYG9v3/OZMUIURFhYGJs4cSIDwAICAtjRo0dZU1MT32VxfvvtNwaAAWBqamp8l9MtX3zxBVezubk53+X0ymBc78NJbW0t27dvH/feDQwMZHFxcXyX1U5BQQF78cUXmYqKCrOxsWH//ve/WUVFBd9lMcZoG+8PQ6Ht643ubEu///47c3V1Zerq6ty0qampA1ZjfX09CwkJYS4uLmzEiBFszZo1TCwW92geFF4J75KTk9mMGTOYQCBgwcHB7Pr163yX1CV/f/92jUJNTQ0bM2YMmzdvHk9VdV2Dq6vroG/AO1rvikAR/veK4vTp02zq1KlMIBCwZ555huXm5vJdEpNKpeyLL75gOjo6zMrKiu3du1ehdopbU9S2ZTAbCm1fb3TWXkZFRTGBQMDeeustVlNTw+7evcssLCwGNLzKtbS0sAMHDjBbW1umoaHB3nrrLVZbW9ut5yrGTyWQYUkqleKDDz6Ah4cH6uvrER0djb/++kvhv3bsCGMMMpkMMpms1/PQ0tKCj48PrzX01pPWPpj15Xof7OsxMDAQUVFROHLkCK5fvw5HR0fs3buXt3qysrLg6+uL999/H6+//joyMzOxZs2aQdVjgCK0LWToOHz4MBhj2Lx5M7S0tDB69GiIxWJeLnxWUlLCihUrkJmZic8++ww//fQTJkyYgLi4uMc+V3kA6iOknbKyMixbtgyxsbHYvn07Nm3apDA/O9cb2trayMrKGvY1DEe03ttbuHAhAgMDsWXLFqxfvx7R0dHYuXMn1NTUBqyGyMhIBAcHw8rKCgkJCXB0dBywZfcl2r5IXxKLxQCgUBdYqqqq4uWXX8aSJUvwwgsvwM/PD3v37sWKFSs6fQ6FVzLgSktLMWPGDFRWViIyMhLu7u58l0QI6WMaGhr44osv4O/vj7/97W/Iy8tDWFjYgATYS5cuYe7cuXjqqadw4MCBfu0snZDBpKWlhe8SOmVqaorw8HB8+OGHWLlyJRoaGrB27doOpx28h7rIoCSVSrFgwQLU19cjNjZWoYNrZmYmFi5cCJFIBKFQCF9fX0RFRbWbLjQ0FAKBgLs1NDRw4xobG7FlyxaMHz8empqa0NfXx9NPP43jx49zjcj27dshEAhQV1eH6Ohobj7Kysodzv/WrVtYtmwZDAwMuGF79+7ttIZHX9O8efMgEomgqamJGTNmIDo6mhv/8ccfc/No/TXj6dOnueEjR47khj+udrmSkhK88sorsLGxgaqqKgwNDREcHIykpKRer/fuktcoEAhgYWGB+Ph4+Pv7Q1tbu8N1IFdWVobXX38do0ePhqqqKvT09DB37lxcvHiRm6az//2jwx88eIDly5dDV1cXBgYGmD9/fpujad1Zj93ZlhTRnDlzcP78eVy7dg0bNmzo9+Xl5ORg4cKFWLBgAQ4fPqyQwVVR2hbgYZt86NAhBAQEwMTEBBoaGnB2dsY333zT5lSFnm7Tcq3fR2pqarCwsMCsWbOwf/9+1NfXt5m2J+1Eb9Z5Z21fZWVlm9cmEAjw8ccfc+un9fAlS5b0eNndWQc9bXtbv66ebEvHjh0D8HDnUiAQwNvbu8evpz8JBAJs3boV77//PjZu3Nh5299/p+IS0t4XX3zBNDQ0WEZGBt+ldOnOnTtMV1eXmZubs7Nnz7KamhqWkpLCZs+ezWxsbDo8ET4oKIgBYPX19dywtWvXMpFIxM6ePcskEgkrLCxkb775JgPALl682Ob5QqGQTZ06tdOa5PP38/NjFy9eZHV1dSw2NpaNGDGClZSUdFoDYw8vWhCJRGzGjBksKiqK1dTUsPj4eObi4sJUVVXZpUuXulWLu7s7MzAwaDe8q9rz8/OZtbU1MzY2ZidPnmQ1NTUsLS2N+fn5MXV1dXb16lVu2t6s9+5ydXVlQqGQTZ48mV29epXV1tZ2ug4KCgqYra0tMzY2ZmFhYayqqordunWLBQcHM4FAwPbs2dNm3p2td/nwoKAgbpnnzp1jGhoazNPTs0frsSfbkiIKCwtjAoGAnTx5sl+Xs3DhQmZvb88aGhr6dTm9pWhtS1hYGAPA/t//+3+svLyclZSUsP/85z9MSUmJvfnmm53W0p1tWv4+MjExYWFhYay6upoVFhayjz76iAFgX3/9NTdtT9qJnuhJ2xcYGMiUlJTY3bt3281n8uTJ7Jdffunx8nuyDhjrWdvbV9uSopo3bx4bP348k0ql7cZReCUDRiqVMktLS/b222/zXcpjLV26lAFgf/75Z5vheXl5TE1NrduNgq2tLZsyZUq7ae3s7HodXsPDwx87TUfhFQCLiYlpMzwlJYUBYK6urt2qpTfh9fnnn2cA2jX8BQUFTE1Njbm7u3PDerPeu0u+DhITE9sM72gdrFq1igFgv/32W5tpGxoamJmZGdPQ0GCFhYXc8MeF17CwsDbDlyxZwgBwOx1yXa3HnmxLimr+/PnM39+/3+b/4MEDpqSkxEJDQ/ttGU9K0dqWsLAwNn369HbDV6xYwVRUVFhVVVWHtXRnm5a/jw4dOtRu/nPmzGkT3HrSTvRET9q+M2fOMADspZdeajNtVFQUMzc371UvFT1ZB4z1rO3tq21JUd2+fZsJBAJ26tSpduMovJIBc+vWLQZA4bvCYowxbW1tBoDV1NS0G+fs7NztRmHjxo0MAHvxxRdZTExMh3uQct0Nr6WlpY+dpqPwqq6uzmQyWbvnmJmZMQAsPz//sbX0JryKRCKmpKTU7kOQMcb1DSrv468367275EdeO/LoOhCJRAwAq66ubjftc889xwCwn3/+mRv2uPDaOugyxthrr73GALDk5OQ2w7tajz3ZlhTVgQMHmKqqar/V/ssvvzA1NTXW3NzcL/PvC4rYtnRE3k/qo0c8e7JNd/U+elRP2ome6Gnb5+zszDQ1Ndu0s0FBQWzbtm09XjZjPVsHjPWs7e2rbUmRubi4sH/961/thtM5r2TAlJeXAwAMDQ15rqRrjY2NqKmpgbq6OrS0tNqNNzIy6va8duzYgZCQENy7dw/+/v7Q0dHBnDlzcPTo0V7XJxQKe/U8+Tmyj5K/nuLi4l7X1JnGxkZUVVVBJpNBJBK1O6/sxo0bAB7+7GhfrvfO6Orqdji89TqQ16yurg5tbe120xobGwMACgsLu71ckUjU5m9VVVUA6FH3R/2xLQ00IyMjNDU1oaampl/mX1FRAZFI1O6ca0WhiG1LVVUVtmzZAmdnZ+jp6XHvzbfeegsAIJFIOnze47bpx72PWutJO9EbPWn7Xn31VUgkEnz//fcAHv4q1IULF7Bu3boeL7cn66A38+7v9lIRjBw5EmVlZe2GU3glA8bGxgYAkJ6ezm8hj6GmpgZtbW00NDSgtra23Xh5CO8OgUCA5557DufPn0dlZSVCQ0PBGENwcDC++uqrdtP2p6qqqg6Hyxvu1o2dkpISmpqa2k1bWVnZ4Tw6q11NTQ26urpQVlZGc3Mz2MNve9rdZsyY0afrvTNlZWVgHfwidut1oKamBpFIhIaGhg5DVlFREQA80e9yd6arbaAn25KiSktLg76+fqc7EU/K2toaJSUlKC0t7Zf5PylFbFuefvppfPTRR3jxxRdx+/ZtyGQyMMbw9ddfA0CH75fueNz76NFpu9tO9EZP2r5nn30WxsbG+O6779DY2Igvv/wSzz//PPT09Hq83J6sA7nutr0D0V7yTSaTITMzE7a2tu3GUXglA8bExAS+vr749ttv+S7lsebOnQvg4ZWerZWWluLWrVvdno+uri4yMzMBACoqKggICOCu+jx58mSbaTU1Nds0WuPGjcPu3bt7+xLaqa2tRXJycpthqampyM/Ph6urK0xNTbnhpqamyMvLazNtYWEhcnJyOpx3V7UHBwdDKpV2eEX/Z599BisrK0ilUgB9t94709DQgPj4+DbDOloHixYtAoB2/6PGxkZERERAQ0MDgYGBT1zPo7pajz3ZlhRRY2Mjdu3ahWXLlvXbMmbOnAkdHZ0+fd/0NUVqW1paWhAdHQ0TExO88sorMDQ05ILuoz0B9Ib8fRQeHt5u3IQJE/Daa69xf/ekneipnrR9ampqeOmll1BcXIwvv/wSv/zyCzZv3tyr5QI9WwdAz9re/m4v+RYWFoaCggJuHbbR7ycsENLKxYsXmZKSEtu7dy/fpXTp7t27TF9fv81VnOnp6SwwMJAZGRl1+1wikUjE/Pz8WHJyMmtoaGBFRUVs69atDAD7+OOP2zx/zpw5TCQSsZycHHb16lWmrKzcpleG7pyr1NU5r0KhkPn4+LDY2Ngur7RnjLFNmzYxAOzbb7/lfkJw2bJlzNzcvMNzXruqvaioiI0ePZqNGjWKhYeHs8rKSlZWVsZ27tzJNDU121zI0Jv13l3yq479/f173NtAdXV1m94Gdu/e3a313tnwd955p8OLx7pajz3ZlhTRq6++yrS1tdn9+/f7dTkfffQR09TUVNgeTRStbZk5cyYDwD7//HNWUlLCJBIJu3DhArOysmIA2Llz5x5bC2Mdb9Py95GpqSk7ceIEq66uZmKxmG3cuJEZGxuz7OxsbtqetBM90dO2jzHGSkpKmIaGBhMIBCwoKKhXy5XryTpgrGdtb19tS4qotLSUWVlZsb/97W8djqfwSgbce++9x5SVldkff/zBdyldunXrFlu4cCHT0dHhuoE5ceIE8/f3ZwAYALZmzRp29OhR7m/57dlnn2WMMZaUlMTWr1/P7O3tmaamJtPX12fe3t5sz5497S4gyMzMZL6+vkwoFDJLS0u2Y8cOxhhjMTEx7eb/6H5nZzXIL7oAwMzNzdm1a9fYjBkzmJaWFtPQ0GB+fn4sKiqq3WuvrKxka9euZaampkxDQ4P5+Piw+Ph45u7uzs3vnXfeeWztcmVlZez1119no0aNYioqKszQ0JDNnj273QdjT9Z7T8l/4zwjI4MFBgYybW3tLtdBaWkpe/XVV5mtrS1TUVFhIpGIBQYGsoiIiMeu947+Z//zP//DGGPthrf+zfqu1mNPtiVF89FHHzElJSX266+/9vuympqa2JQpU5iVlRV78OBBvy+vNxSlbWHsYVBbv349s7S0ZCoqKszY2JitWrWKvfvuu9wy3d3de71NP/o+MjU1Zc888wy7fft2u/XSk3bicXrb9sm9+OKLDAC7fPlyj5f9qJ6sg562vU+yLaGDXhgUQVVVFfPy8mI2NjasuLi4w2kEjPXyhBZCnsBrr72G//znP/jggw/w3nvvDeqfhiWDg5ubG0pLS5Gbm8t3KcOGRCLBSy+9hIMHD+K7774bkB8pAB6e7zdz5kwUFRUhNDQUXl5eA7JcMnTs27cPO3bsQEJCAt+lDCv37t3DggULUF5ejkuXLsHOzq7D6SgxEF58/fXX+O677/DJJ5/A19cXN2/e5LskQkgfioyMhKurK44fP44TJ04MWHAFAH19fURGRmLChAnw9fXFRx991OvzJcnwtHPnTrz++ut8lzGs7Nu3DxMmTICKigri4uI6Da4AhVfCo40bN+L69euQSqVwdXXF+vXruSu5CSGDU05ODlauXInp06fDzs4OKSkpmDNnzoDXoaOjg5MnT+K7777Dtm3b4Ojo2OFFM4QAwN69e7Fo0SLU1tZi586dqKio6NeLC8l/xcTEwM/PD2vWrMEzzzyD6OhoWFpadvkcCq+EV05OToiJicHevXsRFhaGMWPGYPPmze2utiTkUY/2BdnRbevWrdzvuycnJyMvLw8CgQDvvfce3+UPOVlZWdi8eTPs7Oxw7do1HDp0CCdPnoSFhQVvNQkEAqxbtw4pKSlwdXXFvHnz4OPjgwsXLvBWE3ky3X3f90ZoaCj09PTwww8/4Pfff++0v+D+rGE4uXnzJpYtW4YpU6ZAVVUV8fHx2LVrFzQ1NR/7XDrnlSgM+R7vV199hfLycixatAhr1qzBzJkz6ZxYQhRQU1MTjh07hp9++glnz56FnZ0d3nnnHaxYsUIhfyjg0qVL+OCDDxAZGYkpU6Zg/fr1WLZsGdTV1fkujZBhoaWlBadOncLOnTtx6tQpTJgwAdu2bcOsWbN6NB8Kr0ThNDQ04LfffsOPP/6I6Oho2NjYYPXq1Vi9evVjv0oghPS/tLQ0/Pjjjzh48CAqKiowZ84crF27FgsWLBgUO5qXL1/Gjh07EBoaCm1tbaxatQrr1q3DuHHj+C6NkCGpoKAAP/74I/bs2QOxWAx/f3+89NJLWLhwYa9+oIfCK1FoN2/exE8//YQDBw6gpKQE06ZNQ1BQEBYsWIBRo0bxXR4hw0ZSUhKOHz+O0NBQJCYmYsyYMVi9ejWef/55mJub811erxQWFnIfqDk5OZgyZQoWL16M4OBgWFtb810eIYNaaWkpQkNDceTIEZw/fx4ikYjbURw7duwTzZvCKxkUmpubcfLkSfz5558IDw9HRUUFnJ2dsWDBAgQFBcHDw6Pff16VkOGkubkZly9fxvHjx3H8+HFkZ2fD3NwcCxYswPLlyzFt2rQh856TyWQ4ffo0fvvtN5w4cQKVlZXw8PBAcHAwFi9e3OVVz4SQ/8rPz8fRo0dx5MgRXL58Gaqqqpg9ezaWLVuGxYsXQ01NrU+WQ+GVDDpSqRSRkZHch+r9+/dhYmKCGTNmYPr06fDz86Ov/wjpoZaWFiQnJ+PSpUu4dOkSIiMjUVVVBRcXF24n0d3dfcgE1s40NTXhwoULOHLkCEJDQ1FSUoLx48dj1qxZ8Pf3x/Tp06Grq8t3mYQoBIlEgujoaERERCAiIgI3btyAUCjEU089hcWLF2Pu3LnQ0tLq8+VSeCWDXkpKCsLDw3H58mVERUWhtrYWpqamXJD18/PD+PHj+S6TEIXyaFi9cuUKKisrMXLkSEybNg0zZszAvHnzYGtry3epvGlpacGVK1cQHh6OCxcuIDExEQKBAO7u7pg5cyb8/f0xdepUaGho8F0qIQNCKpUiPj6eC6sxMTFobGzE+PHj4e/vj8DAQAQEBPT7RZAUXsmQ0tLSgqSkJERFRSE6Ohrnzp1DZWUldHR04OzsDHd3d+7m6OjId7mEDJj8/Hxcv36du0VHR6OiogKGhobw8vKCj48PZs2ahQkTJgyKi674UFNTg7i4OJw/fx7nz5/HjRs3MGLECNjZ2cHd3R0+Pj6YOnUq7O3taR2SIaF1uxEdHY2rV69CIpHA2NgY06ZNw6xZsxAYGDjg54hTeCVDmlQqxfXr13Ht2jXEx8cjISEBt27dgkwmg6mpKTw8PODh4YGJEyfC0dERNjY2Q/5rUTK0SaVS3L17F2lpabhx4wa33VdWVkJFRQXOzs7w9PSEh4cHvL294ejoSNt8L4nFYly5cgVxcXG4du0aEhMT0djYCH19fXh5ecHLywseHh5wdnaGlZUV3+US0qXi4mKkpKTgxo0biI2NxbVr15CXl4cRI0bA0dERXl5e8Pb2xpQpU3j/NpPCKxl2qqurcePGDSQkJCA+Ph7x8fG4f/8+AEBLSwv29vZwcnKCg4MDnJ2d4eDgQF10EYUjk8lw7949pKWlISMjg7u/efMmmpqaMGLECIwbNw4eHh7w9PSEp6cnXF1dqU/TftTU1ITExETExcVxgfbu3bsAAF1dXTg7O8PZ2RkuLi5wcXGBk5MTtLW1ea6aDDcNDQ3IyMhASkoKUlNTuXv5L1yamZlxO1/e3t5wd3fvl/NWnwSFV0IAVFVVcQEgPT2duxUUFAAARCIRxo8fjzFjxrS7jRw5kufqyVCWl5eHu3fvIisrC3fv3uVumZmZqK+vh0AggI2NDRwcHODk5ARHR0c4OjrC3t6ezsVUAFVVVUhNTW0TElJTU1FdXQ2BQABbW1uMGzcOdnZ2sLOzw9ixYzF27FhYWVnRqQfkieTn5+P27du4c+cOd7t58ybu3r2LlpYWaGhowNHRsc1OlbOzM4yMjPgu/bEovBLShfLy8jZHtOQh4v79+2hqagLwMNjKg+zo0aMxatQoWFhYwNLSElZWVgq3x0oUS3l5OXJzc5GTkwOxWIx79+61Car19fUAAE1NTYwePZrb1saPH899Q0Db2ODz4MEDLsjeunWLCxllZWUAADU1NYwZMwZjx46FnZ0dRo8eDSsrK1hZWcHGxqZbP6FJhrbGxkbk5ORwt6ysrDZBtba2FgCgra3N7RjZ2dnByckJLi4uGDNmDEaMGMHzq+gdCq+E9EJLSwvEYnGbI2Ly+/v373ONBvDw60ILCwtYW1vD3Nyce2xhYQETExMYGRnR0dshSCaTobi4GCUlJcjLy0Nubi5yc3ORnZ3N/Z2dnQ2JRMI9R19fH7a2ttyOUOt7MzMzHl8NGSjl5eVckG19f+/ePVRVVXHTGRgYcGHW2toa1tbWsLS0hIWFBczNzWFsbNxnfWqSgdfc3Izi4mIUFBQgLy8P2dnZyM7O5nZyc3JyuG8GAUBDQwOjR4/mjty3DqsmJiY8vpL+QeGVkH5QWVnJhRN5aJE3Orm5uRCLxWhoaOCmV1FRgZGREYyNjblAa2JiAmNjYxgZGcHU1BQGBgbQ09ODvr4+hEIhj69u+KqurkZ5eTkqKipQXFzM3fLz81FSUoKioiIUFBSgpKQExcXFkMlk3HO1tLRgZWXFBQz5kXkLCwtuh4aOppGuVFVVIScnp02Qkd8ePHiAwsLCNtucZZs2gAAAIABJREFUnp4eTE1NYWxsDDMzMxgZGcHc3JxrU4yMjKCvrw99fX3a9gZAY2MjysvLUVZWhrKyMuTn53NtRmFhIQoLC1FQUIDi4mLu/FM5Y2Njbmfl0R0WKysrGBoa8vSq+EHhlRCeyIOPvNEqKSlBYWEhioqKuD1u+TRSqbTNc1VVVaGvrw89PT0u0D76WFtbG1paWhCJRNDU1IRQKIRIJIKWlhY0NTWH3VfN1dXVqKurg0QiQWVlJWprayGRSFBbW4uqqirU1NSgoqICFRUVXECV38sft7S0tJmnmppamyBgZGQEMzMzGBoathluampKHduTftfU1ISCggLk5+dzO1VFRUVcKCoqKuLGNTY2tnmuuro69PX1YWBgwAVa+U2+46yjowMtLS0IhULo6OhAJBJBKBRCS0trWFx4VldXh7q6OtTW1qKiooJ7XFdXh4qKCm7nVh5QW9+Xl5ejrq6uzfyUlZW5AxXytkK+c2FmZsYdzLCwsKALLR9B4ZWQQaCkpIRrAB8XsOSPa2trUVNT0+V8dXV1uWCro6PDDRMIBNDU1ISamhqUlZW5D6ZHx8mpqKh0Goa7GlddXd0uEMpVVVW1OYokkUjQ2NgIqVTKvS75NB2Nk3+4SCSSbq0HLS2tLncGHn1saGhIgZQMWmVlZSgtLW0XsB69ycfJw1ln71cA7cKskpISRCIRN05JSQlCoRCqqqpQV1eHhoZGm/YFAEaMGMG1RY+SP+dRTU1N7YKhXG1tLZqbm7m/5W1FY2MjJBIJWlpaUF1dDeDhN2aMMe458p3b2tpablxn5AcM5OH/0Z2Ajv42MjKibup6icIrIUPUyZMnsW7dOshkMmzfvh1eXl7cEUaJRMIdLWgd8BhjqKysBPDfRl/eyHc0Tk7+gdCRrsY9GoK7Gif/4Gr9gaitrQ1lZWWoqalBU1OzzbhHg7m2tjaEQiGEQiF0dXVx7NgxbNmyBdbW1ti/fz88PDx6uIYJGX7q6+tRV1eH6upqVFVVceFOHvDkj+vq6tDc3Iza2to2bYc8AMvbhUeDp7y96UhNTU27b6EAQCAQdLojKW8b5DQ0NKCurs7tVLd+ro6ODkaMGMG1Perq6tDS0uKOMsuPOmtra3M7vPJhZGBReCVkiKmursZbb72FPXv2YMmSJfjhhx9gYGDAd1kKKTs7G2vWrMHly5fxxhtv4MMPP6SLXAghRMFReCVkCDl79izWrl2LxsZG/PDDDwgODua7JIXHGMOePXvwxhtvwMbGBvv374e7uzvfZRFCCOkE9YBMyBAgkUiwefNmzJkzB97e3khPT6fg2k0CgQDr1q1DSkoKDA0N4e3tjXfffZfrx5cQQohioSOvhAxyUVFRWL36/7d353FN3en+wD8JRJawBJRdRJRFWUUQi6AWqFpcKjAtTqutS6vUttNtpqN3bq917nRmpIsz7dx2rG1Hb63TV2s72tpCXYrtAGpBBDFsiqCyh50EEiDJ9/eHv5xLCCAgcAI879crL5JvTs55cki+5zlPvuecrWhqasI777yDxx9/nO+QJixdFfbll1/G3LlzcfjwYYSEhPAdFiGEkF6o8krIBKVUKrF7924sX74cvr6+kEqllLjeo95VWIlEgsWLF2P37t16B6cRQgjhF1VeCZmAzp8/j61bt0ImkyElJQU7duzgO6RJR6vV4qOPPsJLL70Eb29v/O///i+Cg4P5DosQQqY8qrwSMoHoqq3Lli3D3LlzIZVKKXEdI0KhEDt27EBOTg7MzMxw3333ISUlZdDzXBJCCBl7VHklZIK4ePEitmzZgrq6OrzxxhuUtI4jtVqNt99+G6+99hpCQkJw6NAhzJs3j++wCCFkSqLKKyFGTqVSYffu3YiKisLs2bNx9epVSlzHmampKXbt2oXc3Fyo1WosXLiQqrCEEMITqrwSYsSys7OxZcsWVFdX480338T27dvpcoI801Vh9+zZg7CwMBw6dAg+Pj58h0UIIVMGVV4JMUI9PT1ISUlBVFQUZsyYgcuXL2PHjh2UuBoBXRX20qVLUKlUWLBgAVJSUqDVavkOjRBCpgSqvBJiZAoKCrBlyxaUlpZiz549eOWVVyAU0n6mMerp6cH+/fuxZ88eLFq0CIcPH4aXlxffYRFCyKRGW0RCjIRarUZKSgoWLVoES0tL5OfnY9euXZS4GjGRSIRdu3YhJycHHR0dCA4OxjvvvAOqCRBCyNihyishRkAqlWLLli0oKirCa6+9RtXWCainpwd//OMf8frrryMmJgYfffQRZs2axXdYhBAy6dDWkRAe6aqtYWFhmDZtGvLy8qjaOkGJRCLs3bsXWVlZqKysRGBgIA4ePEhVWEIIGWW0hSSEJ4WFhViyZAn27t2L3//+98jIyICvry/fYZF7tHjxYuTl5WHnzp145plnEBcXh6qqKr7DIoSQSYOSV0LGmVarxTvvvIPQ0FAIhUKu2mpiYsJ3aGSUmJubY9++fcjIyMDNmzcREBCAgwcP8h0WIYRMCpS8EjKObty4gfvvvx+vvPIKdu/ejaysLLpS0yQWERGBvLw8PP3009i5cyfWrFmD6upqvsMihJAJjZJXQsYBYwwHDx5EcHAw2trakJ2djb1791K1dQqwsLDAvn378O9//xvXr1+nKiwhhNwjSl4JGWMVFRWIjo7Gc889h+eeew45OTlYsGAB32GRcRYZGYn8/HwkJydj586dWLduHWpra/kOixBCJhxKXgkZI7pqa1BQEJqbm3Hx4kXs27cP06ZN4zs0whNLS0vs27cPP/74I4qLixEcHIwvv/yS77AIIWRCoeSVkDFw8+ZNxMbG4tlnn8Wzzz6LS5cuYeHChXyHRYzE0qVLcfnyZSQkJCApKQlJSUlobGzkOyxCCJkQKHklZBT1rrbKZDJcuHCBqq2kXzY2Nvjggw+QlpaGixcvIiAgAMePH+c7LEIIMXqUvBIySm7duoWVK1fi2WefxTPPPIPc3FyEhYXxHRYxcqtWrcLVq1exfv16JCYmIikpCU1NTXyHRQghRouSV0JGwbFjxxASEoKamhpkZWVh3759MDMz4zssMkHY2trigw8+QGpqKs6fP4+AgAB8/fXXfIdFCCFGiZJXQu5BXV0dHnroIfzyl7/EI488gpycHISHh/MdFpmg4uLiIJVK8dBDDyE+Ph5JSUlobm7mOyxCCDEqAkYX3iZkRI4dO4ann34aEokEhw4dwrJly/gOiUwi3333HXbs2MGNo167di3fIRFCiFGgyishw1RfX4+EhARs2LABDz/8MAoKCihxJaNuzZo1kEqleOCBB7Bu3To88cQTkMvlfIdFCCG8o8orIcNw7Ngx7Ny5EzY2Nvj4448RHR3Nd0hkCjh27BieeeYZiMVifPzxx4iNjeU7JEII4Q1VXgkZAplMhl/84hfYsGEDfvGLX6CgoIASVzJuHnnkERQWFiI0NBQrVqxAcnIyFAoF32ERQggvqPJKyF1Q1YsYE6r+E0KmOqq8EjKAlpYWPPHEE0hKSkJcXByuXr1KiSvhna4KGxwcjNjYWCQnJ6Ojo4PvsAghZNxQ5ZWQftCR3mQioDNeEEKmIqq8EtJLa2srkpOTsXbtWkRGRkIqlVLiSoyWrgrr7++P6OhoJCcno7Ozk++wCCFkTFHllZD/Ly0tDdu3b4dGo8GBAwewfv16vkMiZMiOHTuG5ORkuLi44NChQ3SxDELIpEWVVzLpSaXSQZ9va2tDcnIyVq9ejSVLlkAqlVLiSiacRx55BHl5eXB1dUVkZCR2796Nrq6uQV9zt+8GIYQYI0peyaT27bffIjw8HKWlpf0+f+rUKQQGBuLrr7/Gv/71L3zxxReYPn36OEdJyOjw8PDA6dOn8d577+H9999HaGgoLl261O+0p06dQnh4OCWwhJAJh5JXMmlVVlZi06ZNUKlU2LRpEzQaDfdce3s7kpOTERcXh/vuuw9SqRQJCQk8RkvI6BAIBNixYwcKCgrg6OiIiIgI7N69G93d3dw0ra2t2Lx5M1QqFRITE+lsBYSQCYWSVzIpqdVqJCUlobOzE4wx5OXl4a233gIAZGRkYOHChTh+/Di++OILfPHFF5gxYwbPERMyumbPno0ffvgB7733Ht577z2EhYXh8uXLAIAXXngBTU1NYIyhoqICTz75JM/REkLI0NEBW2RS2rVrF95++229aqtIJMKjjz6KI0eOIDExEe+//z4cHR15jJKQ8XH9+nVs3boVOTk5ePLJJ/H3v//dYJrDhw9j8+bNPERHCCHDQ8krmXTS0tKwZs0a9P1oi0QiODo6IiUlBRs3buQpOkL4odVq8ac//Qn79+9HW1sbtFqt3vPm5ubIzc2Fn58fTxESQsjQ0LABMqlUVVXhscceg0AgMHiup6cHdXV1qKio4CEyQvglFApRWFgIhUJhkLgCd4baJCQk0HliCSFGjyqvZNJQq9VYtmwZLl26hJ6engGnMzExQXZ2NhYuXDiO0RHCr2+++eaup4AzNTXFpk2bcOjQoXGKihBCho8qr2TS2LNnD7KzswdNXIE7R2Nv3rz5rtMRMlk0NjZi27ZtEAoH7/LVajUOHz6MI0eOjFNkhBAyfJS8kknh1KlT2Ldvn94BWv0xNTUFYwxSqRQHDx4cp+gI4dfevXvR1NQEoVAIExOTQacVCARITk5GSUnJOEVHCCHDQ8MGyIRXX18Pf39/tLS0GIzlEwgEMDExgVqthkQiQWxsLFauXIkVK1bA09OTp4gJGX/l5eU4e/Yszpw5g9OnT6O9vR3Tpk2DWq02+N6IRCLMmTMHly9fhqWlJU8RE0JI/yh5nQLkcjnUajXUajXkcrleGwAwxtDa2jrg6+/2PHDnSGULC4shP29tbQ1TU1OYmprC2tpar204NBoNli9fjgsXLkCr1UIgEMDU1BQ9PT2wtLTE/fffj1WrViE2Nhb+/v7Dmjchk5VGo0Fubi5++OEHnD59GhcuXEBXVxfMzMy4S8oKBAJs3boVH3/88YiXo1QqoVKp9PqelpYW7vnOzs4BL2Hb3d096MUTButzevcrAGBrawuhUAhLS0uYmZlBJBLByspqJG+JEGIEKHk1AlqtFi0tLWhtbUVraytaWlqgUCigVCohl8shl8uhVCqhUCjQ3t4OpVKJjo4OtLW1QalUorOzkzv1TX+J6kTTX0JraWkJCwsL2NraQiwWw8LCAjY2Nrhy5Qp+/vlnAHcOxJo7dy5CQ0OxePFihISEYPr06ZBIJLCzs6MKEiEDUKlUOHv2LE6dOoUffvgBpaWlXDV227Zt8PHxQXt7Ozo6Ori+Ry6Xc49bW1uh1WrR1tYG4M4V7O42hMdY2NnZAfi/vsba2hpisRhisRgSicTgsZWVFffYzs4ONjY2sLe3526EkLFHyeso0mg0aGxsRENDAxobG1FXV4eGhgYuMe37V3e/vb293/npqgPW1tawsLAwuG9jYwNzc3PuvomJCVdZEAqFsLW1BYBB23SsrKwgEokGfG93e753JXcoz+uS7a6uLu7UPIO1KRQKqFQqtLe3c4n97du3ce3aNVhYWHBDA1Qq1YBJ+7Rp07hEViKR6N23s7ODnZ0dZsyYAQcHB8yYMQNOTk5wdHSEWCwe8H0RYow6OjpQV1fH9UE1NTVoampCc3PzgLf+vr8mJiYQCARwcXGBvb39gEmcRCKBQCDg/ur6CzMzM1haWur1PboqKIBBK6C6+Q1ksD5HV/HV0VV7FQoFenp6uD5Go9Fw/a+ur2lra+OS8vb2doMkXaFQcI/7i7l3Itv3Nn36dDg5OcHFxQUODg5wdXWFjY3NgO+RENI/Sl7vgjGG+vp6VFdXo7q6GlVVVZDJZGhoaEB9fT0aGhq4ZLWxsVHvxPhCoRAODg6wt7cfNGnq77mR/IQ+1ajV6gHXkVwuh0KhMNhRGGwnorm5GY2NjQYbJQsLCzg4OMDJyYlLbB0cHODs7AxnZ2e4u7vDxcUF7u7ugw6dIOReKZVK3Lp1C7dv30ZlZSWqq6shk8lQU1MDmUyG+vp61NbWGnyGZ8yYgRkzZgyaWPW96XaIgcG/a1OZrvjQ3Nx8150DXf/S0NCgN8bY3NycS2gdHR25fsXZ2RmzZs2Ch4cHZs2aRUkuIb1M6eSVMYaamhpUVFSgqqoKNTU1qKysRE1NDaqrq1FZWYna2lq9Uyrp9pwdHBzg6OgIR0dHg0qdbkPh4ODQ78nyiXHr7OxEY2Ojwc6Jroql2wDV1taivr4e3d3d3Gvt7e3h6uoKd3d3uLq6YubMmXBzc4Orqys8PDwwZ84cGr5ABtTc3IyysjIuQb19+zZu3bqFyspKVFZWoqGhgZtWLBbD3d1dL+FxdHSEq6srt3Olq/BNmzaNx3dFetNoNFwBpLq6Gg0NDairq0NtbS1XJZfJZKiurtY71sDW1lYvmXV3d+cee3l5wcnJicd3Rcj4mvTJq0qlQk1NDcrLyw1uJSUlehUKOzs7zJkzBy4uLnB1deX+6trc3d1p75cYaGlpQU1NDWpra7m/5eXlBm06us9Zf7dZs2ZRhWuSa2lp6bc/0t10en9OevdFuscuLi60czzJ9d5+9e5bdI9v3rzJDbEyMzODm5sb/Pz84O/vr9eveHp60meFTCqTInlVq9UoLy9HUVERSkpKUFxcjOLiYpSVlXFjnYRCIdzc3ODp6QlPT0/uC6277+zsfNcTeBMyUh0dHbh58ybKy8tRUVGBiooKvfsKhQLAnYPVZs2aBV9fX/j5+WHevHmYP38+5s+fTweDTDCtra24evUqpFIpCgoKIJVKUVxcjKamJgB3xnt6enrCy8sLPj4+8PLygre3N7y8vODu7j7oGHNCgDsH+9bU1OD69esoKysz+Ksb92tjYwMfHx8EBQUhICAAgYGBCAwMpGotmbAmVPKqVqtRUlKCgoICvUS1rKwM3d3dEAgE8PDw4Db83t7eXJLq4eGhd4ASIcZEJpNxCa3uV4Hi4mKUlpZyia2DgwP8/f3h6+uL+fPnw8/PDyEhIZgxYwbP0U9tWq0WJSUluHz5Mq5evcolrJWVlQDu/NyrSxh0/ZK3tzc8PDyoyk7GDGMMlZWVXDJbXFzM7Ujphp84ODhwiWxAQABCQkIQFBREO07E6Blt8trT04Nr164hNzeXu+Xl5aGzsxMikQju7u56P4/4+flhwYIFdO4+Mum0tLSgsLAQRUVF3N/ePzG7uLggNDQU/v7+8PPzQ2hoKPz8/OhnwjFSU1Oj1y+dP38ezc3NEIlE8Pb25v4Pur/0vyDGpm+fkpubiytXrkChUHCf46ioKERGRlJ/QoySUSSvWq0WUqkUGRkZuHjxIvLy8lBSUgKNRgMbGxssWLAAISEhCAkJwYIFC+Dn50d7hmTKk8lkyMvL07uVlZWBMQZ7e3ssXLgQYWFhiIyMRGRkJHc+SzJ0Wq0WV65cQXp6OjIzM5GdnY2amhoIhULMnz8fixYtQnh4OMLDw6liRSY03S8I2dnZ3K2goAA9PT2wt7dHeHg4IiIiEBMTg8WLF9NnnfCKl+RVqVQiJycHmZmZyMrKQlZWFtra2mBjY4OIiAgsXLiQS1bnzp1Le3yEDJFcLkd+fj6XzGZnZ6O4uBgCgQB+fn5cNWXp0qXw8PDgO1yjVFRUhHPnziE9PR0//vgjmpub4eDggKVLl2Lx4sUIDw9HaGio3hWcCJmMVCoV8vPzkZ2djZycHPz000+orKyEWCzG0qVLER0djZiYGISEhHCnVSNkPIxL8qrVapGbm4u0tDScOnUKly5dQnd3N9zc3LB06VJuYxoQEEBfAEJGWVNTE7KysridRd33b+bMmYiOjkZcXBxWrlyJ6dOn8x0qLzo7O3H69GmcOHECp06dQl1dHWxtbbFs2TLExMQgJiYGgYGBtBNNCICysjKkp6fj3LlzOHfuHOrr6yGRSBATE4P4+HisXbuWfuUhY27Mktfm5macPn0aaWlp+P777yGTyTBz5kzExcVh2bJliIqKwuzZs8di0YSQQfT+5ePs2bPIzMyEVqtFeHg41qxZg7i4OISEhEzqZK25uRnffvstl7CqVCpERERg7dq1iImJQWhoKO1IE3IXjDEUFRUhPT0dqampSE9PB2MMy5cvR3x8POLj4+Hm5sZ3mGQSGtXktbm5GZ9//jk+++wznD9/HgKBAFFRUXjwwQcRFxeHoKCg0VoUIWSUtLe348yZM9yOZnV1NZydnZGQkIBNmzYhIiJiUiSy3d3d+Prrr/Hxxx/jhx9+gFAoRExMDBISErB+/Xo6bRAh96itrQ2pqak4ceIE0tLSoFAosHjxYmzduhWPPvooDbUho+aek9euri6kpqbik08+QWpqKkQiERISEhAfH48HHniAu541IcT4McZw5coVpKam4rPPPoNUKoWXlxc2btyITZs2wcvLi+8Qh+3GjRv48MMPcfjwYTQ2NiIuLg6PPfYYVq9eTf0TIWNEpVLh7Nmz+Pzzz/Hll19CJBLh0UcfxY4dOxAaGsp3eGSCG3Hyeu3aNfztb3/DP//5T7S2tiImJgaPP/44EhMT6XRVhEwS+fn5OHLkCD777DPU1dUhIiICO3fuRFJSktFfcvT06dN488038cMPP8DNzQ1PPvkknnzySbi7u/MdGiFTSnNzM44cOYKDBw+iqKgIoaGhePnll7FhwwYankNGhg3TxYsX2bp165hQKGRz585lKSkprKqqarizGXOfffYZA8AAMDMzM77DmRTefPNNbp26ubnxHY5Rm2zrSq1Ws7S0NLZhwwYmEomYq6srS0lJYQqFgu/QDJw5c4aFh4czAGzlypXsm2++YWq1mu+wGGPUL42FyfZdm+wyMjLYo48+ykxMTJi3tzc7evQo02q1fIdFJpghJ6+FhYVs3bp1DABbsmQJO378ONNoNGMZ26iIjY012EjI5XLm5eXF1qxZw1NUE1twcDBtJIZoMq6r27dvs9/+9rfM2tqaOTk5sXfffZf19PTwHRYrLy/n+qi1a9ey7OxsvkMaEPVLo28yftcms+vXr7OtW7cyExMTtnjxYpaTk8N3SGQCEd6tMqtSqfC73/0OCxYsQFVVFVJTU5GVlYX4+HgIhXd9uVFijEGr1UKr1Y54HlZWVoiKihrFqAiZGNzd3ZGSkoIbN25g06ZNeOWVVxAWFoaff/6Zt5gOHDiAoKAgVFRU4OzZszh58iQWLVrEWzwjQf0SmUq8vLzwj3/8A7m5ubCwsEBERAReffVV9PT08B0amQAGzT7Ly8sRGRmJ999/H/v370dOTg7i4uLGK7YxY21tjRs3biA1NZXvUAiZsBwcHPDWW2+hoKCAO4n/22+/DTaO1z3p7u7Gtm3b8Nxzz+H5559Hbm4uYmNjx235o4n6JTIVBQcHIz09He+++y7++te/Ii4uDi0tLXyHRYzcgMlrfn4+7rvvPmi1Wly6dAnPPfccDawmhBjw8fHhDo76j//4DyQnJ99T9XCoNBoNNm7ciGPHjuFf//oX/vjHPxr9QWSEEEMCgQA7d+7E+fPnUVZWhuXLl1MCSwbVb/JaWVmJFStWICgoCJmZmRPi9DglJSWIj4+Hra0td+m6zMxMg+lOnDgBgUDA3VQqFfdcV1cX9uzZg3nz5sHS0hL29vZYt24dvvnmG2g0GgDAW2+9BYFAgI6ODmRlZXHzMTU15eajVqvx+eefY8WKFXB2doaFhQUCAwPxzjvv6G3U+8Zy8+ZNbNiwARKJBNOnT8fatWtx48YNg/fQ1NSEl19+GXPnzoWZmRlmzpyJBx54AIcPH4ZSqdSbtqGhAc8//zxmz56NadOmwcHBAYmJicjPzx+Vdb5mzRrY2trC0tIS0dHRyMrKAgC0trbqvTeBQIDXX3+dWz+92x9++OEhL3O46+z111/npu39c+r333/Ptc+YMWPA+d+6dQsbNmyAtbU1pk+fjscffxwtLS24efMm1q1bB2tra7i4uGD79u2Qy+UjWlc6Q/3cGCOBQIAXXngBX375JT755BP87ne/G/Nl/vrXv0ZaWhpOnz6Nhx56aMyXNxLUL02Nfmk462C4fZLufysQCDBz5kzk5OQgNjYW1tbW4/7+xlpQUBB++ukntLS0YMOGDeP6Kw6ZYPobCBsbG8v8/f1ZR0fHeI6/HbHr168ziUTC3Nzc2OnTp5lcLmcFBQVs5cqVbPbs2f0e1bt+/XoGgCmVSq7tqaeeYra2tuz06dOss7OT1dXVsd/85jcMADt37pze68ViMYuMjOw3npMnTzIA7E9/+hNrbm5mDQ0N7N1332VCoZD95je/GTCW9evXs/PnzzOFQsHOnDnDLCws2KJFi/Smra2tZZ6enszZ2ZmdPHmStbe3s7q6OvaHP/yBAWB/+ctfuGlramqYh4cHc3JyYt999x2Ty+VMKpWy5cuXM3Nzc3b+/PnhrGZOcHAws7W1ZdHR0SwzM5PJ5XKWk5PDgoKC2LRp09iPP/7ITbtq1SomFApZWVmZwXwiIiLY0aNHRxTDcNYZYwP/v0JDQ9n06dMHnH9iYiK7dOkSUygU7JNPPmEAWFxcHFu/fj3Ly8tjcrmcHThwgAFgL730ksF8hrOuhvu5MVaHDx9mQqGQ/fTTT2O2jIsXLzKhUMg+/fTTMVvGvaJ+aWr1S8NZB4wNv08KDg5mYrGYRUREcP+P8e53x0t2djYzNTVlhw4d4jsUYqQMktfs7GwGgGVkZPARz4g88sgjDAD78ssv9dqrq6uZmZnZkDcSnp6ebMmSJQbT+vj4DHsjcf/99xu0b9q0iYlEItbW1tZvLCdPntRrf/jhhxkA1tDQwLVt2bKFAWCff/65wfwffPBBvQ5y8+bNDIBBR1VbW8vMzMxYaGhov/HfTXBwMAPALly4oNdeUFDAALDg4GCu7dSpUwwAe+aZZ/SmzczMZG5ubqy7u3tEMQxnnTE28uT1u+++02v39/dnAAwSM09PT+a4yk3eAAAU/0lEQVTr62swn+Gsq+F+boxZbGwsW7169ZjN/7HHHmOLFy8es/mPBuqX7pgq/dJw1gFjI0teAbC8vDy99vHsd8fTtm3b9N4TIb0ZJK9//vOf2ezZs/mIZcSsra0ZACaXyw2eCwwMHPJGYufOnQwA2759O7tw4cKg54YcbCMxEN35CPtWFnSx1NXV6bW/9NJLDAC7cuUK12Zra8sAsPb29rsuz9bWlgmFwn6TnoULFzIArLKycljvgbE7nai5uXm/5+ZzdXVlAFhNTQ3XFhgYyCwtLVljYyPXtn79erZv375hL7v364e6zhgbefJaX1+v175ixQoGwOBXiaioKGZtbW0wn+Guq/4M9LkxZocPH2bm5uZjdjo9d3d39uabb47JvEcL9Uv9m6z90nDWAWMjr7z2Z7z63fGUmprKALDW1la+QyFGyGDMa2Nj44S6xndXVxfkcjnMzc37vbKXo6PjkOf13nvv4ZNPPkF5eTliY2NhY2ODBx98EMePHx9WTG1tbdizZw8CAwNhZ2fHjTF65ZVXAACdnZ39vq7vpSp1B5/oxqN1dXWhra0N5ubmd71GtG5arVYLW1tbg3FQly9fBgBcv359WO9NZ/r06f1e7163vmUyGdf24osvorOzE++//z6AO1dnS09Px44dO0a07N7uts7ulY2Njd5joVAIExMTWFpa6rWbmJgMuMyhrquRfm6MkbOzM1QqFRQKxZjMv7m5WW9coLGhfql/k7VfGs46uBcSiaTf9vHud8eDg4MDgDvfdUL6Mkhe586di9LSUr0DBoyZmZkZrK2tB9xQDueDLxAI8Pjjj+Ps2bNobW3FiRMnwBhDYmIi9u/fbzDtQNatW4c//OEP2L59O65duwatVgvGGP7yl78AwIgHoZuZmcHW1hYqlWrQg4N000okEpiamqKnpwfsTpXd4BYdHT2iWNra2vpt13WevTfOGzduhJOTE/7nf/4HXV1dePvtt7F582bY2dmNaNkjIRQK0d3dbdDe2to65sse6roaq88NH/Lz8+Ho6GiQ/I+W2bNno6ioaEzmPRqoXxp42snYLw1nHeiMpE9qamrq9/9krP3uvSgsLIRIJIKbmxvfoRAjZJC8JiYmQqlU4qOPPuIjnhHRnXv2+++/12tvbGxEaWnpkOcjkUhQUlICABCJRFixYgV35O13332nN62lpaVex+Pr64uDBw9Co9EgKysLzs7OeP755+Hg4MBtUPoecTsSCQkJANDvuSBDQkLw0ksvcY8TExOhVqsNjmoHgJSUFMyaNQtqtXpEcSgUCly5ckWv7erVq6ipqUFwcDBcXFy4djMzMzzzzDOQyWR4++23cfToUbzwwgsjWu5Iubi4oLq6Wq+trq4Ot2/fHvNlD2VdjfXnZjwpFAr8/e9/x2OPPTZmy0hMTMSRI0eGnCjwgfqlO6ZKvzScdQCMrE9SqVTIycnRazPmfnektFotDhw4gHXr1tHp70j/+htL8OqrrzJLS0uWm5s7hiMWRk9ZWRmzt7fXO6q3sLCQrVq1ijk6Og55bJmtrS1bvnw5u3LlClOpVKy+vp7t3buXAWCvv/663usffPBBZmtry27fvs3Onz/PTE1NWVFREWOMsZiYGAaAvfHGG6yhoYF1dnay9PR0NmvWLAaAnTlz5q6xMMbYrl27DAbo645odXFxYd9++y1rb29nlZWVbOfOnczJyYndunWLm7a+vp7NnTuXzZkzh6WmprLW1lbW1NTEDhw4wCwtLfs9sGAodGOvoqKi2MWLFwc96lWnoaGBWVhYMIFAwNavXz+i5fY2nHXGGGPPPfccA8D+9re/MblczsrKylhSUhJzc3MbdMxr3/mvWrWKmZiYGEy/fPnyfsejDWddDfdzY4w0Gg177LHHmKOj413H8t4LmUzGZsyYwbZt2zZmy7hX1C9NrX5pOOuAseH3SbqzKcTGxt71bANj8f7G01tvvcVEIpFBP06ITr/Ja09PD1u5ciWzt7c3OHLTWJWWlrL4+HhmY2PDncrl22+/ZbGxsQwAA8CefPJJdvz4ce6x7rZx40bGGGP5+fksOTmZzZ8/n1laWjJ7e3t23333sQ8//NDgIICSkhK2dOlSJhaLmbu7O3vvvfe45xoaGlhycjJzd3dnIpGIOTk5sS1btrDdu3dzywwNDWUXLlwwiOU///M/GWPMoL339c4bGxvZiy++yDw9PZlIJGIuLi7sl7/8Jbt27ZrBemlqamIvv/wymzNnDhOJRMzBwYGtXLlyRImQ7sAOAMzNzY1lZ2ez6OhoZmVlxSwsLNjy5ctZZmbmgK/fvn17v0fqD8dI11lrayt76qmnmIuLC7OwsGBRUVEsJyeHhYaGctPv2rVrwPnn5OQYtP/5z39mGRkZBu2vvfbaiNbVUD83xqqrq4tt3ryZmZubj0ui/fXXXzMTExPu/2+MqF+aGv2SznDWwVD7JJ3g4GDm5ubGioqK2KpVq5i1tfW4v7/xcOTIESYUCtkbb7zBdyjEiPWbvDLGmFKpZGvXrmXTpk1j77zzTr9HcBIyHP/4xz+MOvkiI1deXs4WL17MrKys2Pfffz9uy/3444+ZiYkJ27x5s0GFkJChmCj9ki55Ha6J8v40Gg37/e9/zwQCAfvtb3/LdzjEyA14eVhzc3N8/fXX+K//+i/8+te/xtKlS1FQUDDQ5ITc1YEDB/Dyyy/zHQYZRd3d3di3bx8CAgKgVCpx6dIlrFq1atyWv23bNpw8eRInTpzAwoULkZ2dPW7LJpPDZO+XJsL7Ky8vR3R0NF5//XW8//77SElJ4TskYuQGTF6BO0dDvvrqq7h06RK0Wi1CQkKQlJQ04tOYkKnlo48+QkJCAhQKBQ4cOICWlhYkJSXxHRYZBVqtFseOHYOfnx/++7//G6+88gqys7Ph6+s77rHExcWhuLgYc+fORUREBJ544gnU1taOexxkYpjs/dJEen8dHR3Yu3cvAgIC0NzcjAsXLuDpp5/mOywyEQy1RKvRaNg///lP5uvry0xNTVlSUhK7ePHiWFaFyThBn3Fs/d1ee+21Yc/3ww8/ZACYqakpCwoKGvQAwLGKgYyu9vZ2tn//fjZ79mwmEonY9u3b2e3bt/kOi/PNN98wT09PJhaL2fPPP8+qqqr4DomMEPVLd/Qe06u7DTbOezjvjy9yuZz99a9/Zc7OzszOzo7t27ePqVQqvsMiE4iAseGd3E+tVuPYsWPYv38/Ll26hIULF+Lxxx/Ho48+OqEubkAIGRqtVosff/wRR44cwVdffQXGGLZu3YoXX3wRc+bM4Ts8A0qlEh988AHefPNNNDc34+GHH8b27duxbNkyvkMjZEorKCjAhx9+iE8//RRarRa/+tWv8NJLL2H69Ol8h0YmmGEnr71lZmbi0KFD+Oqrr9DR0YGVK1di48aNiI+PN7gCESFkYpFKpfj0009x9OhRVFVVISwsDJs2bcITTzwxIU50rlKpcPToURw8eBDZ2dmYN28etm/fjieeeMKor85FyGTS0dGBzz//HB9++CEuXrwIb29vPPXUU9i+ffuE6EeIcbqn5FVHqVTim2++waeffopTp07B3NwcK1euRFxcHOLi4uDq6joasRJCxpDuxPFpaWn47rvvIJVKMXv2bGzcuBGbNm3CvHnz+A5xxPLz83Hw4EEcPXoUXV1dWLVqFeLj4/HQQw9R1YeQUdbR0YG0tDScOHECJ0+eRFdXFxISErBjxw7cf//9g14JjpChGJXktbeGhgZ8+eWX+Pbbb3Hu3DmoVCosWLCAS2QjIiJgYmIymoskhIxQXV0d0tLSkJaWhjNnzqC1tRU+Pj5YvXo1EhMTERUVNak2NB0dHTh27Bi++uornD17Fj09PVi2bBkSEhIQHx8Pd3d3vkMkZEJqamrCyZMncfz4cZw5cwbd3d2IjIxEYmIiNm7cSL92kFE16slrb0qlEj/++CNSU1ORlpaGGzduQCKRICoqCpGRkYiKikJYWBjMzc3HKgRCSC+3b99GRkYGzp8/j4yMDEilUpibm2P58uVYvXo1Vq9ejblz5/Id5riQy+VIS0vD8ePHkZqaCrlcjqCgIMTExCA6OhrLly+HjY0N32ESYpRUKhXOnz+Pc+fOIT09HT///DNEIhFiY2ORkJCAhx56CA4ODnyHSSapMU1e+7p27RpOnTqFzMxMZGZmoqamBmZmZggLC+MS2iVLltDPeISMAo1GA6lUiszMTGRlZSEjIwNVVVUQiUQICwvDkiVLEBMTg/vvv3/Kj1Hv6urCuXPncOrUKaSnp+Pq1asQCoUICwtDdHQ0YmJiEBkZOeXXE5m6enp6kJ2djfT0dJw7dw4XLlyASqWCl5cXoqOjsWLFCjz44IOwtrbmO1QyBYxr8tpXTU0NsrKyuI1rXl4etFotXFxcEBoayt38/f2N8qhmQoyFWq1GaWkpcnNzuVt+fj46OjpgZWWF++67j/u1IzIyEhYWFnyHbNQaGxtx4cIFZGVl4ezZs7h8+TKEQiF8fX31+qbw8HBMmzaN73AJGXU1NTXIzc3lttGXL1+GUqmEs7Mzli5digceeAArV67E7Nmz+Q6VTEG8Jq99NTU14eeff8bly5eRl5eHvLw8VFRUAACcnJwQEhKChQsXYsGCBZg3bx58fHxgZmbGc9SEjK+6ujoUFxejsLCQ+64UFhaip6cHYrEYwcHBCAkJQUhICBYtWoSAgAAIhYNej4TcRU1NDTIyMpCdnY3s7GxcvnwZnZ2dsLKyQmhoKBYtWoRFixYhMDAQ3t7eMDU15TtkQoaEMYaKigpIpVLk5uZyn/Hm5maIRCIEBwcjPDwc4eHhWLJkCby9vfkOmRDjSl7709rayiWyug11aWkpNBoNTExM4Onpifnz52PevHmYN28e/Pz8MG/ePEgkEr5DJ2TENBoNKioqUFxcjOLiYpSWlqKoqAglJSVobW0FANjb23NJqu7m4+NDB0SOA7VajcLCQvz888/cxr6oqAgajQZmZmbw8/ODv78/AgMDERgYiICAADoYjPBOJpPh6tWrkEqlkEqluHr1KgoLC6FQKAAAXl5eXKIaHh6OkJAQOiaFGCWjT177093djdLSUpSUlKCkpATFxcUoKSlBaWkpOjs7AQDOzs7w8fGBp6cnPD09MWfOHO4vnbqLGIPOzk5UVFSgvLxc7++NGzdw/fp1dHd3AwDc3d31ds58fX0xf/58ODs78/wOSG8qlQpFRUVcQlBQUACpVIrq6moAgEQiwfz58+Hj4wNvb294eXlxf+nAMDJaVCoVrl+/jrKyMly/fp27X1RUBJlMBgCYPn06goKCEBAQgICAAAQGBsLf358+h2TCmJDJ60AYY7h58yZXpbpx4waXENy8eRNdXV0AAHNzc4OkdubMmXBzc4ObmxtcXV1pHBu5Z01NTaipqUFlZSVqa2tx8+ZNvSS1rq6Om9bR0VFvB0uXoPr6+tIBEBNcS0sLV+0qKipCWVkZysrKcOvWLajVagB3hkV5e3tzNw8PD8yaNQuzZs2Cq6srDUMgHMYYamtrcfv2bVRWVuLWrVt6yWpVVRUYYxAIBJg5cya3g+Tr68v9CuDi4sL32yDknkyq5HUwjDFUV1ejoqKi32pXXV0dNBoNN72TkxNcXFy4pNbV1RXu7u5wcXGBq6srZsyYAQcHB4hEIh7fFeFDe3s76urq0NDQgJqaGr0EVfe3qqoKKpWKe421tTU8PDz0dph63xeLxTy+I8KHnp4eVFRUcNUxXQJSVlaGyspK9PT0AABMTEzg4uLCJbTu7u5wd3eHh4cH3Nzc4OzsTH3RJKHVaiGTySCTyVBVVYXKykpUVlbi9u3bXLJaVVXF/Sqj+2zoEtTe1Xxvb2/6yZ9MWlMmeb0bjUaD+vp6vSSkpqYG1dXVqKqq4hIU3bAEHXt7ezg6OnLJrG5Dors5OzvDzs4OEokEEomEfpYxMj09PWhtbUVLSwtaWlrQ0NCAxsZGyGQy1NfXc491yWpDQwNXwQcAoVAIJycnvR0cV1dXg50eKysrHt8lmWi0Wi3q6upw8+ZNLoGprKzUe9zY2Kj3GkdHRzg6OsLZ2RnOzs5wdHSEm5sb1+bg4AB7e3vY29vTztI4UqlUaG5uRnNzM/drjEwmQ11dHWprayGTybg2mUymV0SxtbXldlT67rjoqvK000KmIkpeh6mtrQ21tbVobGxEQ0MDl9T0l/A0NDRAq9Xqvd7ExIRLZCUSCezs7PSSW12bWCyGhYUFJBIJLCwsYGFhATs7O1hYWMDc3HzKXxNapVJBqVSipaUFSqUSKpWKu69UKtHa2gqFQoHW1lYuOdXd7/24o6PDYN5isRgODg5wcnLidkJ0iYHuse45Jycn2ngQXiiVSlRWVnL9Tu9EqL6+Xi8p0lXqdMzMzGBvbw87Ozsuoe17s7a2hlgshrW1NWxtbSEWiyEWi2FjYwMbG5spcWAgY4zrSzo6OtDR0YGWlhbuvlwuR3NzM1paWrgEte9NqVTqzdPU1JTrO1xdXQ12OHRtbm5uVOwgZACUvI4hrVaLxsZGLlHqm0D1rvj1bevs7DSo8valS2olEgksLS1hZmYGU1NTboyklZUVRCLRXdt0xGLxgGN9TUxMBuxIe3p6uKNV+9Pe3q5XTeg9vVwuh1qtvmtba2urXmI62MdWIBBAIpHAyspKb4eg7w5Cf48dHBzoRPRk0mlqakJjY+OAiVZ/bQqFQm/oS1/m5uYQi8WwtbWFtbU1TE1NuX5o2rRpEIvF3HcRADeNbgdcRzdtf/pOqzNYn6PbsdXp7u5GR0cHl4gC/9fH6HZ81Wo15HI5tFot2traIJfL0dHRMWgfLBKJYG1tPeAOwEDtjo6Ok+qSy4TwgZJXI9c7aetbWezs7IRSqURbWxs6OjrQ3d3NddTAnSqxVqvVa9Mlkr3bdFpaWgaMo+8Goa/BKsF9N0C9E2Fdwtxfm1AohK2tLQDAxsYGFhYW3MbSwsIClpaWA1amCSH3TpfU6fqYjo4OtLe39/tYq9VySaGuv9BoNGhvbwfwf/2Rrq/S6fu4t747vr0N1Of03dHuvaMukUggEAi4PkaXOPfua3S/fInFYlhZWRk81u0Y00G9hPCHkldCCCGEEDJh0GV3CCGEEELIhEHJKyGEEEIImTAoeSWEEEIIIROGKYBjfAdBCCGEEELIUPw/9Qs6LqyzBqEAAAAASUVORK5CYII=\n",
+      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -678,11 +643,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    }
+   ],
    "source": [
+    "print(npartitions)\n",
     "df_w_numba, df_w_cupy, df_w_cudf = task_graph.run(out_list)\n",
+    "#df_w_numba = task_graph.run(out_list)\n",
     "df_w_numba = df_w_numba.compute()\n",
     "df_w_cupy = df_w_cupy.compute()"
    ]
@@ -696,7 +671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -733,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -766,18 +741,19 @@
       "        -------\n",
       "        cudf.DataFrame\n",
       "        \"\"\"\n",
-      "\n",
-      "        df = pd.read_csv(self.conf['path'],\n",
-      "                         converters={'DTE': lambda x: pd.Timestamp(str(x))})\n",
-      "        df = df[['DTE', 'OPEN',\n",
-      "                 'CLOSE', 'HIGH',\n",
-      "                 'LOW', 'SM_ID', 'VOLUME']]\n",
+      "        df = cudf.read_csv(self.conf['path'])\n",
+      "        # extract the year, month, day\n",
+      "        ymd = df['DTE'].astype('str').str.extract(r'(\\d\\d\\d\\d)(\\d\\d)(\\d\\d)')\n",
+      "        # construct the standard datetime str\n",
+      "        df['DTE'] = ymd[0].str.cat(ymd[1],\n",
+      "                                   '-').str.cat(ymd[2],\n",
+      "                                                '-').astype('datetime64[ms]')\n",
+      "        df = df[['DTE', 'OPEN', 'CLOSE', 'HIGH', 'LOW', 'SM_ID', 'VOLUME']]\n",
       "        df['VOLUME'] /= 1000\n",
-      "        output = cudf.from_pandas(df)\n",
       "        # change the names\n",
-      "        output.columns = ['datetime', 'open', 'close', 'high',\n",
-      "                          'low', \"asset\", 'volume']\n",
-      "        return output\n",
+      "        df.columns = ['datetime', 'open', 'close',\n",
+      "                      'high', 'low', \"asset\", 'volume']\n",
+      "        return df\n",
       "\n"
      ]
     }
@@ -798,7 +774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -820,7 +796,7 @@
     "import dask_cudf\n",
     "\n",
     "from gquant.dataframe_flow import Node\n",
-    "# from librmm_cffi import librmm as rmm\n",
+    "import rmm\n",
     "\n",
     "\n",
     "class PointNode(Node):\n",
@@ -872,17 +848,16 @@
     "        number_of_blocks = ((len(df) - 1)//number_of_threads) + 1\n",
     "        # Inits device array by setting 0 for each index.\n",
     "        # df['distance_numba'] = 0.0\n",
-    "        darr = cuda.device_array(len(df))\n",
+    "        darr = rmm.device_array(len(df))\n",
     "        distance_kernel[(number_of_blocks,), (number_of_threads,)](\n",
-    "            df['x'].to_gpu_array(),\n",
-    "            df['y'].to_gpu_array(),\n",
+    "            df['x'],\n",
+    "            df['y'],\n",
     "            darr,\n",
     "            len(df))\n",
     "        df['distance_numba'] = darr\n",
     "        return df\n",
     "\n",
-    "\n",
-    "raw_kernel = cupy.RawKernel(r'''\n",
+    "kernel_string = r'''\n",
     "    extern \"C\" __global__\n",
     "    void compute_distance(const double* x, const double* y,\n",
     "            double* distance, int arr_len) {\n",
@@ -891,7 +866,8 @@
     "        distance[tid] = sqrt(x[tid]*x[tid] + y[tid]*y[tid]);\n",
     "        }\n",
     "    }\n",
-    "''', 'compute_distance')\n",
+    "'''\n",
+    "               \n",
     "\n",
     "\n",
     "class CupyDistanceNode(Node):\n",
@@ -901,17 +877,21 @@
     "                         'y': 'float64'}\n",
     "        self.addition = {'distance_cupy': 'float64'}\n",
     "        self.delayed_process = True\n",
+    "               \n",
+    "    def get_kernel(self):\n",
+    "        raw_kernel = cupy.RawKernel(kernel_string, 'compute_distance')\n",
+    "        return raw_kernel\n",
     "\n",
     "    def process(self, inputs):\n",
     "        df = inputs[0]\n",
-    "        # cupy_x = cupy.asarray(df['x'].to_gpu_array())\n",
-    "        # cupy_y = cupy.asarray(df['y'].to_gpu_array())\n",
+    "        # cupy_x = cupy.asarray(df['x'.to_gpu_array())\n",
+    "        # cupy_y = cupy.asarray(df['y'.to_gpu_array())\n",
     "        cupy_x = cupy.asarray(df['x'])\n",
     "        cupy_y = cupy.asarray(df['y'])\n",
     "        number_of_threads = 16\n",
     "        number_of_blocks = ((len(df) - 1)//number_of_threads) + 1\n",
     "        dis = cupy.ndarray(len(df), dtype=cupy.float64)\n",
-    "        raw_kernel((number_of_blocks,), (number_of_threads,),\n",
+    "        self.get_kernel()((number_of_blocks,), (number_of_threads,),\n",
     "                   (cupy_x, cupy_y, dis, len(df)))\n",
     "        df['distance_cupy'] = dis\n",
     "        return df\n",
@@ -938,12 +918,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1004,7 +984,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1053,7 +1033,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1088,7 +1068,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/05b_customize_nodes_with_ports.ipynb b/notebooks/05b_customize_nodes_with_ports.ipynb
index 88521331..de652bb4 100644
--- a/notebooks/05b_customize_nodes_with_ports.ipynb
+++ b/notebooks/05b_customize_nodes_with_ports.ipynb
@@ -250,7 +250,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -278,7 +278,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -348,11 +348,11 @@
      "text": [
       "HEAD dist_euclid_df_w_cudf:\n",
       "          x         y  distance_cudf\n",
-      "0  0.298743  0.859643       0.910073\n",
-      "1  0.241831  0.873547       0.906403\n",
-      "2  0.505219  0.054914       0.508195\n",
-      "3  0.600409  0.167747       0.623402\n",
-      "4  0.202772  0.062324       0.212134\n"
+      "0  0.856288  0.102159       0.862360\n",
+      "1  0.522461  0.000139       0.522461\n",
+      "2  0.852728  0.568951       1.025110\n",
+      "3  0.757722  0.987315       1.244562\n",
+      "4  0.392707  0.126662       0.412629\n"
      ]
     }
    ],
@@ -391,7 +391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -421,7 +421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -430,27 +430,27 @@
      "text": [
       "points_df:\n",
       "          x         y\n",
-      "0  0.887968  0.582714\n",
-      "1  0.146722  0.296758\n",
-      "2  0.391815  0.623228\n",
-      "3  0.882974  0.621067\n",
-      "4  0.794594  0.844349\n",
+      "0  0.994778  0.920240\n",
+      "1  0.536145  0.522197\n",
+      "2  0.552025  0.939834\n",
+      "3  0.597529  0.873719\n",
+      "4  0.374750  0.841134\n",
       "\n",
       "dist_euclid_df_w_cudf:\n",
       "          x         y  distance_cudf\n",
-      "0  0.887968  0.582714       1.062094\n",
-      "1  0.146722  0.296758       0.331048\n",
-      "2  0.391815  0.623228       0.736161\n",
-      "3  0.882974  0.621067       1.079522\n",
-      "4  0.794594  0.844349       1.159442\n",
+      "0  0.994778  0.920240       1.355147\n",
+      "1  0.536145  0.522197       0.748426\n",
+      "2  0.552025  0.939834       1.089963\n",
+      "3  0.597529  0.873719       1.058502\n",
+      "4  0.374750  0.841134       0.920839\n",
       "\n",
       "dist_abs_df_w_cudf:\n",
       "          x         y  distance_cudf\n",
-      "0  0.887968  0.582714       1.470682\n",
-      "1  0.146722  0.296758       0.443480\n",
-      "2  0.391815  0.623228       1.015043\n",
-      "3  0.882974  0.621067       1.504041\n",
-      "4  0.794594  0.844349       1.638943\n",
+      "0  0.994778  0.920240       1.915018\n",
+      "1  0.536145  0.522197       1.058342\n",
+      "2  0.552025  0.939834       1.491859\n",
+      "3  0.597529  0.873719       1.471248\n",
+      "4  0.374750  0.841134       1.215884\n",
       "\n"
      ]
     }
@@ -481,15 +481,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A cuDF series can be converted to GPU arrays compatible with the Numba library via `to_gpu_array` API. The next step is to define a Node that calls this Numba kernel to compute the distance and save the result into `distance_numba` column in the output dataframe."
+    "A cuDF series can be converted to GPU arrays compatible with the Numba library via.to_gpu_array` API. The next step is to define a Node that calls this Numba kernel to compute the distance and save the result into `distance_numba` column in the output dataframe."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import rmm\n",
     "@cuda.jit\n",
     "def distance_kernel(x, y, distance, array_len):\n",
     "    # ii - overall thread index\n",
@@ -546,7 +547,7 @@
     "        number_of_blocks = ((len(df) - 1)//number_of_threads) + 1\n",
     "        # Inits device array by setting 0 for each index.\n",
     "        # df['distance_numba'] = 0.0\n",
-    "        darr = cuda.device_array(len(df))\n",
+    "        darr = rmm.device_array(len(df))\n",
     "        distance_kernel[(number_of_blocks,), (number_of_threads,)](\n",
     "            df['x'].to_gpu_array(),\n",
     "            df['y'].to_gpu_array(),\n",
@@ -583,11 +584,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "raw_kernel = cupy.RawKernel(r'''\n",
+    "kernel_string = r'''\n",
     "    extern \"C\" __global__\n",
     "    void compute_distance(const double* x, const double* y,\n",
     "            double* distance, int arr_len) {\n",
@@ -596,7 +597,7 @@
     "        distance[tid] = sqrt(x[tid]*x[tid] + y[tid]*y[tid]);\n",
     "        }\n",
     "    }\n",
-    "''', 'compute_distance')\n",
+    "'''\n",
     "\n",
     "\n",
     "class CupyDistanceNode(Node):\n",
@@ -631,16 +632,20 @@
     "        }\n",
     "        self.delayed_process = True\n",
     "\n",
+    "    def get_kernel(self):\n",
+    "        raw_kernel = cupy.RawKernel(kernel_string, 'compute_distance')\n",
+    "        return raw_kernel\n",
+    "\n",
     "    def process(self, inputs):\n",
     "        df = inputs['points_df_in']\n",
-    "        # cupy_x = cupy.asarray(df['x'].to_gpu_array())\n",
-    "        # cupy_y = cupy.asarray(df['y'].to_gpu_array())\n",
+    "        # cupy_x = cupy.asarray(df['x'.to_gpu_array())\n",
+    "        # cupy_y = cupy.asarray(df['y'.to_gpu_array())\n",
     "        cupy_x = cupy.asarray(df['x'])\n",
     "        cupy_y = cupy.asarray(df['y'])\n",
     "        number_of_threads = 16\n",
     "        number_of_blocks = (len(df) - 1)//number_of_threads + 1\n",
     "        dis = cupy.ndarray(len(df), dtype=cupy.float64)\n",
-    "        raw_kernel((number_of_blocks,), (number_of_threads,),\n",
+    "        self.get_kernel()((number_of_blocks,), (number_of_threads,),\n",
     "                   (cupy_x, cupy_y, dis, len(df)))\n",
     "        df['distance_cupy'] = dis\n",
     "\n",
@@ -665,12 +670,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -726,7 +731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -740,7 +745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -749,27 +754,27 @@
      "text": [
       "HEAD df_w_cudf:\n",
       "          x         y  distance_cudf\n",
-      "0  0.887968  0.582714       1.062094\n",
-      "1  0.146722  0.296758       0.331048\n",
-      "2  0.391815  0.623228       0.736161\n",
-      "3  0.882974  0.621067       1.079522\n",
-      "4  0.794594  0.844349       1.159442\n",
+      "0  0.994778  0.920240       1.355147\n",
+      "1  0.536145  0.522197       0.748426\n",
+      "2  0.552025  0.939834       1.089963\n",
+      "3  0.597529  0.873719       1.058502\n",
+      "4  0.374750  0.841134       0.920839\n",
       "\n",
       "HEAD df_w_numba:\n",
       "          x         y  distance_numba\n",
-      "0  0.887968  0.582714        1.062094\n",
-      "1  0.146722  0.296758        0.331048\n",
-      "2  0.391815  0.623228        0.736161\n",
-      "3  0.882974  0.621067        1.079522\n",
-      "4  0.794594  0.844349        1.159442\n",
+      "0  0.994778  0.920240        1.355147\n",
+      "1  0.536145  0.522197        0.748426\n",
+      "2  0.552025  0.939834        1.089963\n",
+      "3  0.597529  0.873719        1.058502\n",
+      "4  0.374750  0.841134        0.920839\n",
       "\n",
       "HEAD df_w_cupy:\n",
       "          x         y  distance_cupy\n",
-      "0  0.887968  0.582714       1.062094\n",
-      "1  0.146722  0.296758       0.331048\n",
-      "2  0.391815  0.623228       0.736161\n",
-      "3  0.882974  0.621067       1.079522\n",
-      "4  0.794594  0.844349       1.159442\n",
+      "0  0.994778  0.920240       1.355147\n",
+      "1  0.536145  0.522197       0.748426\n",
+      "2  0.552025  0.939834       1.089963\n",
+      "3  0.597529  0.873719       1.058502\n",
+      "4  0.374750  0.841134       0.920839\n",
       "\n"
      ]
     }
@@ -789,7 +794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -817,7 +822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -859,6 +864,8 @@
     "            pass\n",
     "\n",
     "        max_difference = (df1_col - df2_col).abs().max()\n",
+    "        if isinstance(max_difference, np.float64):\n",
+    "            max_difference = max_difference.item()\n",
     "\n",
     "        if isinstance(max_difference, dask.dataframe.core.Scalar):\n",
     "            max_difference = float(max_difference.compute())\n",
@@ -871,12 +878,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -943,20 +950,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/avolkov/progs/python_installs/miniconda3/envs/py36-rapids0.10/lib/python3.6/site-packages/distributed/dashboard/core.py:72: UserWarning: \n",
-      "Port 8787 is already in use. \n",
-      "Perhaps you already have a cluster running?\n",
-      "Hosting the diagnostics dashboard on a random port instead.\n",
-      "  warnings.warn(\"\\n\" + msg)\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -965,26 +961,26 @@
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3 style=\"text-align: left;\">Client</h3>\n",
        "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
-       "  <li><b>Scheduler: </b>tcp://127.0.0.1:41691</li>\n",
-       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:45520/status' target='_blank'>http://127.0.0.1:45520/status</a>\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:33611</li>\n",
+       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
        "</ul>\n",
        "</td>\n",
        "<td style=\"vertical-align: top; border: 0px solid white\">\n",
        "<h3 style=\"text-align: left;\">Cluster</h3>\n",
        "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
-       "  <li><b>Workers: </b>2</li>\n",
-       "  <li><b>Cores: </b>2</li>\n",
-       "  <li><b>Memory: </b>135.16 GB</li>\n",
+       "  <li><b>Workers: </b>4</li>\n",
+       "  <li><b>Cores: </b>4</li>\n",
+       "  <li><b>Memory: </b>270.39 GB</li>\n",
        "</ul>\n",
        "</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "text/plain": [
-       "<Client: 'tcp://127.0.0.1:41691' processes=2 threads=2, memory=135.16 GB>"
+       "<Client: 'tcp://127.0.0.1:33611' processes=4 threads=4, memory=270.39 GB>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1014,7 +1010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1062,12 +1058,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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8kPKhqqurjxw5sn///oqKiunTp7/xxhtRUVG6ugMVwGiCoAAw3KlUqoyMjP379x8/flylUs2ePXvRokULFy7EF2VCSHl5+bFjx1JTUwsLC83NzV9++eU33nhj6tSpuq4LYPRAUAAYMe7du/fLL78cPXr07NmzHR0d/v7+zz77bEhICI/He6Iu4xeLxRcuXEhPTz9//vyNGzdsbGwWLFiwaNGiuXPn6uvr67o6gNEGQQFg5Ono6Dh37twvv/ySnp5eVVVlbGwcHBw8d+7cgIAAHx8fQ0NDXRc4+IRCYUFBQW5ubnp6en5+vlKp9PLyCgkJefHFF2fOnMlkMnVdIMCohaAAMLL9/vvv6enpGRkZ2dnZLS0tbDbby8vL39/f39//mWee4XK5I3SwQSqVlpWVFfzh5s2bFEU5OzvPnTs3JCRk7ty5+BUDwNBAUAAYPX7//ff8/PyCgoL8/PyioiK5XM5ms7lcrru7u6enp5ub2+TJk52dnceMGaPrSu8nEokqKytv3LhRUlJSVlZWUlJSVVWlUqlMTU19fX39/f39/Pz8/f3Hjx+v60oBnjgICgCjk0KhKCsro0+6paWlpaWllZWV9P/vtra2zn9wdHTkcDg2NjZ2dnbW1tZazRBSqbSxsbG5uVkgENTX11dXV1dVVVVWVlZWVra1tRFC2Gz2pEmT3N3dPTw86HAzceJEPT3cFw5AlxAUAJ4UEonk999/r/xfNTU1YrFYvY2ZmRmHwzE1NTUxMTE3NzcxMRk7dqyJiYmJiQkhxNzcnN6MzWarf3koEonoe053d3fTTQmFQnEP7e3tQqGwsbFRJpOpO7KysnJ0dHT+Xy4uLrgaEWC4QVAAeNJ1dHQ0Nzc3Nja2tLTQ3/jv3btHn+DFYjH9WiqVKpXKe/fu0R/p7Ozs6OigX5uYmLBYLEIIg8EwMzMjhNA5gzZu3DgzMzMzMzNbW1tra2sOh8PhcKytrREIAEYKBAUAGIi0tLTQ0NC2tjb1MAMAjEqY/AMAAACNEBQAAABAIwQFAAAA0AhBAQAAADRCUAAAAACNEBQAAABAIwQFAAAA0AhBAQAAADRCUAAAAACNEBQAAABAIwQFAAAA0AhBAQAAADRCUAAAAACNEBQAAABAIwQFAAAA0AhBAQAAADRCUAAAAACNEBQAAABAIwQFAAAA0AhBAQAAADRCUAAAAACNEBQAAABAIwQFAAAA0Iil6wIAAIaOWCw2MTFRvxUIBOnp6S0tLSEhIe7u7josDGDYwogCAAy1zMxMHo9XVVU1lJ3u2bMnODh4ypQp6iU3b96MiYkJCgrKysry8fG5d+/eUNYDMFIgKADAUGtvb6+trZVKpX3ZuLGxcVA6jYmJUalUSqVSvWT9+vUeHh4ODg4HDx784Ycfxo0bNygd9TRYxQPoEIICAAy1yMjI+vr6vgz1C4XCFStWDEqnTCbT3t6+55Jz586ZmpoSQkxNTQerl54GsXgAHUJQAIBhSiqVLlmyREszFB0dHX0c0hgYrRYPMJQQFABg4DZt2uTm5lZfX79gwQILCws/P7+8vDz12pSUlHfeeWfdunVhYWEff/yxXC6nl7e0tHz77bf5+fmEkOLi4vfff9/FxUUqlcbExFhZWfn5+d25c4cQcuzYsfLy8tbW1tjY2G3bthFCrl27tnLlyvj4+Pfee2/16tV9qfDEiROrVq364IMP4uLi1BMBBw8eXLVqFSEkOTk5Njb2yy+/fGQ7D92XI0eOmJiYODg4EEJEItHGjRuZTGZAQMBDiwcYqSgAgP47f/48IYTL5TKZzHfffTcrKys1NdXS0tLIyKihoYGiqO3btwcGBioUCoqiWltbXV1dZ82apVKp+Hx+UFAQISQlJYWiqMbGxpCQEELImjVrSktLi4qKDAwMli1bRvcSHh4+YcIEdadcLpfP51MUJZPJgoKCHlnkoUOHeDxeR0cHXYO1tTWHw6FXtba2EkI2bdrUl53VtC8URYWGhtrb26u39PT05PF4Dy0eYITCiAIADNz06dMZDEZ8fPzs2bMXLVq0a9cumUyWkJAgEAg2bNjw1ltvsdlsQoilpeX69esvXryYlJQUGBi4YcMGdQscDsfX15cQ8tlnn7m5uU2dOtXX17ewsPDBvrq6um7evFlUVEQIMTQ0fPvtt3uvTSaTrVu3Li4ubsyYMXQNdEDpr172hRBiZGTUc2NjY+MBdAEwnCEoAMDA6enpsVgs+gxKCImIiNDX1y8pKcnLy5NKpfSYPC08PJwQkp2dTR44uTKZTEIIi/V/t3Wxt7cXi8UP9sVms0NDQ9euXbt69WqhULh8+fLea8vJyWlsbPT09FQv0dfX7/8ukt73BWDUQ1AAgEHDZrPt7Oy6u7urq6sJIW1tbepVVlZW9KzE47R/9OjRpUuX7t69m8vlXrx4sfeNy8vL6ZIep0dCiJb2BWCkQFAAgMGkUCi4XK6zszMhhL4msScul/s4jbPZ7MOHDycmJhJCQkND6SigCT1+QJ/mH4eW9gVgpEBQAIBBIxAImpqaIiMjeTyeiYnJ8ePH1avq6+tlMtn8+fP71aCenp5EIqFfy+Xy3bt3E0KioqLy8vJUKlVmZmYvn/Xy8iKEJCcnq5f0vOESRVF9rKH3fWGxWBKJRN2sRCJRqVQPFg8wciEoAMBjkcvlJSUl9OvNmzdHRUXxeDwrK6stW7ZcunQpIyODXrVz587o6Oi5c+cSQpqbmwkh9O8OCCEikYgQ0t3dTb9tbm6mf6dACLGzs2ttbS0sLLxw4YJMJtu/fz99Sra3tzc1NfXx8emlsMDAwODg4AMHDiQkJMhksitXrvD5/JaWlsOHD8tkspqaGkKITCZ75A72vi+enp5CoXDLli0VFRWbNm2Sy+UVFRVXr159sPj+HVaAYYP56aef6roGABh57ty5k5iY6O3tXVZWJpPJkpKS0tLSOBzO9u3bGQwGIcTPz8/b23vHjh0FBQX5+fkWFhbx8fEMBiM7Ozs+Pr6qqkogEDg7O1dXV2/btk0oFEokEj8/v+PHj+/Zs0csFjMYjKCgICcnp9OnT588eZLH43l4eCQmJp46daq+vj4pKSk6OnrBggW9FxkREdHU1LRv376EhISxY8fa2tp6eXkFBARIJJLt27eXlJTU1dVZW1s7OjrSv4zQRNO+EEJ8fHxKS0uTkpJyc3Pj4uIEAgGXy3VycnJ1dXV0dFQX7+3tPYgHH2AoMfo+/gYAoJaWlhYaGhodHZ2cnNzR0aHrcgBAW/CYaQAYwezt7dU3fLzPjz/+GBYWNsTtAIw+CAoAMHBCoVChUEgkkrFjx+qkgLq6umHVDsDog4sZAWDgMjIyVCpVXFwcn8/XdS0AoBUYUQCAgaurqzM3N9d1FQCgRRhRAAAAAI0QFAAAAEAjBAUAAADQCEEBAAAANEJQAAAAAI0QFAAAAEAjBAUAAADQCEEBAAAANEJQAAAAAI1wZ0YA6BOFQtHzgQiNjY2EkKqqqvb2dnoJg8FwdnbWTXEAoDV4zDQA9ElHR8f48ePFYrGmDXg83uXLl4eyJAAYAph6AIA+MTQ0XLhwIZPJfOhaPT295cuXD3FJADAEEBQAoK9WrFihVCo1rV2yZMlQFgMAQwNBAQD6KiQkxMLC4sHlTCZz9uzZHA5n6EsCAG1DUACAvmKxWMuXL2ez2Q+uio6OHvp6AGAI4GJGAOiHS5cuzZw5876FbDZbIBCYmZnppCQA0CqMKABAP8yYMcPOzq7nEhaL9cILLyAlAIxWCAoA0A8MBuOVV17pOfugUqmioqJ0WBIAaBWmHgCgf65fv+7t7a1+a2Rk1NraamhoqMOSAEB7MKIAAP3j5eXF5XLp12w2+6WXXkJKABjFEBQAoN+io6Pp2Yeurq4VK1bouhwA0CJMPQBAv/3++++urq4URZmbmwsEAhYLT40BGLUwogAA/TZx4kQfHx9CyMsvv4yUADC6ISgAwECEhoYSQubOnavrQgBAuxAUAKB/VCrV0qVLt27dSghZvHgx/QIARitcowAA/XPgwIHY2Fj106EYDEZxcbGXl5duqwIALcGIAgD0T25uLoPBUL9lsVi5ubk6rAcAtApBAQD6Z/z48Xp6//+fju7ubjw3EmAUw9QDAPTPnTt3PD09FQpFd3c3m812dnYuLi7GPZcARiuMKABA/7i4uPz222/h4eF2dnZvvfVWTk4OUgLAKIYRBQAYiLS0tNDQ0La2NnNzc13XAgBahBEFAAAA0AhBAQAAADRCUAAAAACNEBQAAABAIwQFAAAA0AhBAQAAADRCUAAAAACNEBQAAABAIwQFAAAA0AhBAQAAADRCUAAAAACNEBQAAABAIwQFAAAA0AhBAQAAADRCUAAAAACNEBQAAABAIwQFAAAA0AhBAQAAADRCUAAAAACNEBQAAABAIwQFAAAA0AhBAQAAADRCUAAAAACNWLouAABg6IjFYhMTE/VbgUCQnp7e0tISEhLi7u6uw8IAhi2MKADAUMvMzOTxeFVVVUPZ6Z49e4KDg6dMmaJecvPmzZiYmKCgoKysLB8fn3v37mn6rE4KBhgmEBQAYKi1t7fX1tZKpdK+bNzY2DgoncbExKhUKqVSqV6yfv16Dw8PBweHgwcP/vDDD+PGjdP02X4VDDDKICgAwFCLjIysr6/vy1C/UChcsWLFoHTKZDLt7e17Ljl37pypqSkhxNTUtPde+l4wwOiDoAAAw5RUKl2yZImWBvw7OjowQgDQFwgKADBwmzZtcnNzq6+vX7BggYWFhZ+fX15ennptSkrKO++8s27durCwsI8//lgul9PLW1pavv322/z8fEJIcXHx+++/7+LiIpVKY2JirKys/Pz87ty5Qwg5duxYeXl5a2trbGzstm3bCCHXrl1buXJlfHz8e++9t3r16r5UeOLEiVWrVn3wwQdxcXHqWYyDBw+uWrWKEJKcnBwbG/vll1/23kgfCwYYnSgAgP47f/48IYTL5TKZzHfffTcrKys1NdXS0tLIyKihoYGiqO3btwcGBioUCoqiWltbXV1dZ82apVKp+Hx+UFAQISQlJYWiqMbGxpCQEELImjVrSktLi4qKDAwMli1bRvcSHh4+YcIEdadcLpfP51MUJZPJgoKCHlnkoUOHeDxeR0cHXYO1tTWHw6FXtba2EkI2bdr0yEb6VTDA6IMRBQAYuOnTpzMYjPj4+NmzZy9atGjXrl0ymSwhIUEgEGzYsOGtt95is9mEEEtLy/Xr11+8eDEpKSkwMHDDhg3qFjgcjq+vLyHks88+c3Nzmzp1qq+vb2Fh4YN9dXV13bx5s6ioiBBiaGj49ttv916bTCZbt25dXFzcmDFj6Bro831/DbhggNEBQQEABk5PT4/FYtFpgBASERGhr69fUlKSl5cnlUodHBzUW4aHhxNCsrOzCSFGRkY9G2EymYQQFuv/butib28vFosf7IvNZoeGhq5du3b16tVCoXD58uW915aTk9PY2Ojp6aleoq+v3/9dJAMuGGB0QFAAgEHDZrPt7Oy6u7urq6sJIW1tbepVVlZW9KzE47R/9OjRpUuX7t69m8vlXrx4sfeNy8vL6ZIep0cAQFAAgMGkUCi4XK6zszMh5MFL/Lhc7uM0zmazDx8+nJiYSAgJDQ2lo4Am9PgBHVkAYMAQFABg0AgEgqampsjISB6PZ2Jicvz4cfWq+vp6mUw2f/78fjWop6cnkUjo13K5fPfu3YSQqKiovLw8lUqVmZnZy2e9vLwIIcnJyeolPW+4RFFUvyoBeGIhKADAY5HL5SUlJfTrzZs3R0VF8Xg8KyurLVu2XLp0KSMjg161c+fO6OjouXPnEkKam5sJIfTvDgghIpGIENLd3U2/bW5upn+nQAixs7NrbW0tLCy8cOGCTCbbv38/faa3t7c3NTX18fHppbDAwMDg4OADBw4kJCTIZLIrV67w+fyWlpbDhw/LZLKamhpCiEwm68s+9r1ggNGH+emnn+q6BgAYee7cuZOYmOjt7V1WViaTyZKSkqbeGzkAACAASURBVNLS0jgczvbt2xkMBiHEz8/P29t7x44dBQUF+fn5FhYW8fHxDAYjOzs7Pj6+qqpKIBA4OztXV1dv27ZNKBRKJBI/P7/jx4/v2bNHLBYzGIygoCAnJ6fTp0+fPHmSx+N5eHgkJiaeOnWqvr4+KSkpOjp6wYIFvRcZERHR1NS0b9++hISEsWPH2traenl5BQQESCSS7du3l5SU1NXVWVtbOzo60r+MeKh+Faynh29fMNowkIIBYADS0tJCQ0Ojo6OTk5M7Ojp0XQ4AaAseMw0AI5i9vb36ho/3+fHHH8PCwoa4HYDRB0EBAAZOKBQqFAqJRDJ27FidFFBXVzes2gEYfTCdBgADl5GRoVKp4uLi+Hy+rmsBAK3AiAIADFxdXZ25ubmuqwAALcKIAgAAAGiEoAAAAAAaISgAAACARggKAAAAoBGCAgAAAGiEoAAAAAAaISgAAACARggKAAAAoBGCAgAAAGiEoAAAAAAa4THTANAnQqFw0qRJ6idKUxTV3d3NZrPVG0yZMqWgoEBH1QGAtuBZDwDQJ2ZmZs8880xaWppKpVIvVD+amcFgPP/88zoqDQC0CFMPANBXUVFRmlZRFLVs2bKhLAYAhgamHgCgr6RSqZWVVWdn533LGQyGu7t7SUmJTqoCAK3CiAIA9JWxsfGLL77Y87oEGovFeuWVV3RSEgBoG4ICAPTDyy+/3N3dfd/C7u7uJUuW6KQeANA2TD0AQD8oFAorKyuxWKxeoqen5+/vn5ubq8OqAEB7MKIAAP2gr6+/ePHinrMPenp6mHcAGMUQFACgf1asWNHV1aV+S1HUokWLdFgPAGgVggIA9M+cOXOsra3p10wmMyQkxMbGRrclAYD2ICgAQP/o6em9/PLL9OwDRVHR0dG6rggAtAgXMwJAvxUUFPj7+xNCDAwMWltbx44dq+uKAEBbMKIAAP3m6+vr6OhICJk/fz5SAsDohmc9ADzRRCKRQqEQi8WdnZ0dHR0SiaSrq0skEnV3d4tEIkKIXC6XyWQ9PyKVShUKhb29fU1NjVKp/PDDD42MjAwMDHpuM27cOCaTqaenZ2pqymazx44da2hoOGbMGBMTExaLZWZmZmBgYGxsPKS7CgADgqAAMAq1t7c3NzcLBILm5uampqa2tjaRSCT8A/26vb1dKBRqmnxkMBhmZmaEECaTOW7cuJ6rDAwMjIyMOjs7mUxmVVVVdXX1vXv3lEqlegOKooRCISFEqVTeu3dPU5FsNtvMzMzU1NTMzMzMzMzc3Fz91srKisPhWFtbjx8/nsPhIFIA6BCuUQAYkTo7O2tqampra2tra6urq2tra5uamuhY0NLS0vOhjjY2NhYWFmY90Cdj+sRMf7kfO3bsmDFjDA0NjY2N9fX1TU1N9fQePS/53XffrVmz5pGbKRQKqVTa0dHR2dmpHrHo7OxURxYaHVzohS0tLe3t7eoWjIyM6MRgbW1tb2/v4ODg4ODg5OTk6OhoZ2fHYuELD4AWISgADHeNjY03b968devWrVu3bt++TYeD5uZmeq2hoaGjo6ODg4OdnZ2NjY2tra36i7iNjY21tTWTydRSYRRFMRgMLTUul8tbWlro9NPS0tLY2CgQCAQCAb379fX19L0cmEymra0tHRomTpzI5XJdXV0nTZpkbm6upcIAnjQICgDDSHd3961bt65fv15aWnrr1q2Kiopbt27R90s2MTGZNGnS008/rf4+Tb9Q39LgiaJSqZqamuihlNra2pqamurq6lu3bv3+++/0aIqVldWkSZPo3ODu7u7l5TVhwgRdVw0wIiEoAOhSW1tbcXHx9evXS0pKrl27Vlpa2tnZyWazJ06cOHnyZFdXV/r78aRJk2xtbXVd7AigVCpramrogEUPw1RUVFRVVVEUZWpq6vUHb29vDw8PXPoA0BcICgBDSqlUlpeXFxYWXrp0ic/n37hxg6Ioc3NzNze3adOmubu70y8MDQ11XenoIRaLKyoqSktLCwsLy8rKioqK7t69y2QyuVzutGnTZs6cGRgYOGXKlL5clgHwBEJQANA6mUzG5/OzsrJyc3MLCwulUqmpqSnvD1OnTuVwOLqu8QlCUVRlZeXVq1cvX76cl5d39erVzs5OS0tLHo8XEBAwb948X19f7V3YATDiICgAaIVSqSwsLExPT09PT8/NzZXL5VOmTJkxY0ZAQACPx8P31+FDoVAUFRXl5eXl5eXl5OTU19ebmprOnj07JCQkJCRk8uTJui4QQMcQFAAGk1QqPXPmTGpq6vnz59vb221tbUP+YGdnp+vq4NFu3LhBx7vs7Ox79+7Z29u/+OKLkZGRwcHB+B0mPJkQFAAGgUgkOn36dGpq6q+//qpQKGbNmjV//vxnn33W3d1d16XBAHV3d1+5cuX8+fPHjh27du2apaXlwoULFy1aFBISoq+vr+vqAIYOggLAwFEUlZWVtXfv3uPHj6tUqrlz50ZGRi5cuPDJ/MniKHb79u3U1NTU1NTffvvN1NQ0Kipq1apVnp6euq4LYCggKAAMhEAg+OGHH/bt23f79m0ejxcbGxsREYGb/Ix6NTU1//nPf77//vvbt28HBASsWrVqyZIlRkZGuq4LQIsQFAD6p6am5osvvjhw4ICRkRG+WT6ZKIrKzMzct2/fsWPHjI2N33vvvbVr1973RAyAUQNBAaCv6urqtmzZ8v3339vZ2X300UfR0dG428ETrqWlZdeuXV9//bWent5f/vKXuLg4ExMTXRcFMMgQFAAeTaFQbNq0KT4+fvz48evXr3/ttddwORuoCYXCr776aseOHSwW61//+terr76q64oABhOCAsAj/Pbbb6+99lpVVdXGjRtXr16t1YggFou1+pVU2+3rqq/hoK2t7fPPP//mm2+ef/75PXv22Nvb67oigMGBW74AaERR1CeffBIQEGBjY3P9+vV3331Xeylhz549wcHBU6ZMod9mZmbyeLyqqqrHabNnI999911QUBCPx3vsSh/tvn0ZdINycAadhYXF119/ffHixdu3b3t4eBw5ckTXFQEMDgQFgIdTKBRLly7dunXrjh070tPTnZ2dtdpdTEyMSqVSKpX02/b29traWqlU2vunGhsbe1nbs5E333xTJBKpVKoB1NZ7Lw+6b18GXR8PDq2/xT+mwMDA4uLiqKio5cuXf/HFF0PZNYC2UADwAKVSuXjx4nHjxmVnZw9Zp8uWLeNwOH3fvr29ffbs2X3f/vnnn588eXJ/q+pvL7T+7ouWDKz4QfHtt98yGIytW7fqpHeAQYQ7kgI8xObNm0+ePHnu3Lng4GBd1/JwUql0yZIl2h5+H5petES3xa9Zs0apVL777rteXl5hYWE6qQFgUGDqAeB+5eXln3/++T//+c8hSAknTpxYtWrVBx98EBcX13OQvKWl5dtvv83Pz6ffXrt2beXKlfHx8e+9997q1asJIceOHSsvL29tbY2Njd22bVt9ff3WrVs9PDza2tqee+45Jyenu3fv3tcILSMjIzQ01MLC4rnnnrtz5w4h5MiRIyYmJg4ODoQQkUi0ceNGJpMZEBDwYC+EEIqiEhIS3n77bX9//9DQ0Fu3bj1yXzQpKyv7+9//7ubmVl9fv2DBAgsLCz8/v7y8PPUGKSkp77zzzrp168LCwj7++GO5XP7gwSkuLn7//fddXFykUmlMTIyVlZWfnx+9Xw8W/+Bh1Kq4uLiXX345Jiamo6ND230BaJGuhzQAhp1XXnnF09NTqVRqu6NDhw7xeLyOjg6KolpbW62trenhej6fHxQURAhJSUmht+RyuXw+n6IomUwWFBRELwwPD58wYQL9+uzZs5MnT2YymZ988snevXv9/Px+/vnn+xp5/vnnLS0tX3/99V9//XXXrl1GRkZ2dnYSiYSiqNDQUHt7e3Vhnp6ePB7vwV4oitqyZcsPP/xAUVR3d7ebmxuHw5FKpb3sSy8uXrzo5ubGZDLffffdrKys1NRUS0tLIyOjhoYGiqK2b98eGBioUCjoBl1dXWfNmqVSqe47OI2NjSEhIYSQNWvWlJaWFhUVGRgYLFu27KHFP/QwalVzc/PYsWO/+eabIegLQEsQFAD+R1dXl6mp6c6dO7XdkVQqtbW1PXz4sHrJokWL1CfX8+fPq8+FCoWCEKI+2ag/ct9Z8I033iCE3L59W72kZyMURT3//PN2dnbqtR9//DEhZMeOHRRFLVy4sGdQ4PF4Dw0K9fX148ePV0eof/zjH4SQn376qfd96cVrr73GYrHoNEBRFP1LgX/84x/Nzc3GxsaJiYnqLQ8cOEAI+fHHHx/cr48++ogQ0traSr+dOXOmq6vrg8VrOozaFh0dravrJAAGBaYeAP5HfX29SCTy9/fXdkc5OTmNjY09b//c87eXPR8fwGazQ0ND165du3r1aqFQuHz58oc2yGazWSzWxIkTH9oIredthl977TVCSGFhYd9rzs3N7erqevPNN2NjY2NjYxsaGmJiYgwNDXvfl14wmUwWi8Vms+m3ERER+vr6JSUleXl5UqmUng2hhYeHE0Kys7Mf3C8mk0kIUT8D2t7eXiwWP9hXHw/joPP39y8tLR2avgC0ARczAvwP+nun+tSlPeXl5X3v6OjRo7Gxsbt3705NTU1OTp41a9bjFzBhwgR9ff1+TZ/fuHHD2Nh437599y3fsWMHGYyDxmaz7ezsuru7q6urCSFtbW3qVVZWVvSsxOO0r43D+EgGBgb0XyqAEQojCgD/w97ens1m37hxQ9sd0d+56TPiI7HZ7MOHDycmJhJCQkND6ZDxmPT09FgsloeHR98/YmRkVFdXV1dX13NhS0tLv/aldwqFgsvl0netoK9J7InL5T5O49o4jI9UWlqq7ZtwAGgVggLA/zA0NJwzZ86hQ4e03ZGXlxchJDk5Wb1E002K5HL57t27CSFRUVF5eXkqlSozM5MQoqenJ5FIBlxAVVVVV1fXkiVLCCEsFksikah7l0gk6lsz9ezF09OToqgPPvhA3cjvv/++a9euvu9L7wQCQVNTU2RkJI/HMzExOX78uHpVfX29TCabP39+vxrsWbymw6hVcrk8JSXlT3/6k7Y7AtAeBAWA+/3lL385c+aMts8igYGBwcHBBw4cSEhIkMlkV65c4fP5LS0thw8flslkzc3NhJDW1lZ64/3799PnXXt7e1NTUx8fH0KInZ1da2trYWHhhQsXZDIZfaYXCoXqLu5rhMlktre30zc0pChq48aNn3zyyeTJkwkhnp6eQqFwy5YtFRUVmzZtksvlFRUVV69eva+XwMBAX1/fw4cPR0ZGJiUl7dq1680331yzZk3v+9L7cZDL5SUlJfTrzZs3R0VF8Xg8KyurLVu2XLp0KSMjg161c+fO6OjouXPnPrhfIpGIENLd3a3ea/rHFw8eooceRq366quv7t69OwQ/xQTQHuann36q6xoAhpenn366pKTk22+/Xbp0ac+r/wZdREREU1PTvn37EhISxo4da2tr6+XlFRAQUF9f/89//rOqqkogEDg7Ozs5OSUmJp46daq+vj4pKSk6OnrBggWEEEdHx9OnT588eZLH4125cmXv3r1SqbSpqWnChAkcDic7Ozs+Pl7diIuLi4eHR11d3Y8//piTk5ORkRESEvLmm2/Slfj4+JSWliYlJeXm5sbFxQkEAi6X6+Tk5Orq2rOXqVOnRkZG1tfXZ2dnnzt3ztDQ8Lvvvhs/fnwv++Lq6spgMDQdgVOnTpWUlMhksqSkpLS0NA6Hs337dnp7Pz8/b2/vHTt2FBQU5OfnW1hYxMfHMxiM+/arurp627ZtQqFQIpH4+fkdP358z549YrGYwWAEBQU5OTmpi/fw8HjoYdSeS5cuvfbaa5999hluuAQjGp4eCfAQ7e3tgYGBDAYjIyODw+HoupxRKzY2NikpaVTej+jKlSuhoaGzZ89OTU3V08PYLYxg+OsL8BDm5uZpaWnd3d3+/v7FxcW6LmcEs7e3t9bg7Nmzuq5OW1JTU+fMmTNjxoyffvoJKQFGOvwNBni4p556Kjc318XFxd/ff+PGjV1dXbquaESqq6tr0SAsLKytrU2hUDzOJZnDTXt7+6uvvvrSSy+9+uqrJ06cMDAw0HVFAI8LQQFAI0tLy4yMjK1bt27dutXf3//atWu6rmhU+eijj86dO6dSqeLi4vh8vq7LGQSnTp3y8PBIT08/derUd999p74HFMCIhmsUAB7tzp07MTExFy5ciIyM/Pzzz+lfCgCoXbp06dNPP01PT1+8ePHu3bstLS11XRHAoMGIAsCjubi4pKenHz58uLS01MPDIzo6+ubNm7ouCoaFixcvzpkzZ+bMmSqVKicn5+eff0ZKgFEGQQGgT/T09JYuXVpSUnLs2LGSkhI3N7dnn302OTkZ1y48mcRi8d69e6dNmxYcHNzV1ZWenp6RkTFz5kxd1wUw+DD1ANBvKpXqxIkTe/fuPX/+/Pjx41977bU33njDxcVF13XBUKBvWfHTTz8plcqXXnrprbfemjFjhq6LAtAiBAWAgaNv3bNr166amho3N7fFixcvW7YMVzCMSqWlpcnJyUeOHCkvL+dyuXQ6tLKy0nVdAFqHoADwuLq7u8+fP5+amnrixIm7d+96e3tHRkbOnz/fy8url5sSwvDX1dWVm5t7/Pjxo0eP1tTUODs7R0ZG0o+i0HVpAEMHQQFg0CiVysuXLycnJ6ekpDQ0NFhbW8+ePTskJOS5555zcnLSdXXQV3fu3ElPT09PT09LSxMKhS4uLuHh4YsXL6Zv1qnr6gCGGoICwOBTqVRFRUX0mebSpUudnZ2TJ08OCQkJCAgICAjAQ4eHG6VSWVZWlpeXx+fz09PTGxoazM3N58yZExISEhIS4urqqusCAXQJQQFAuzo6OujTT1ZWVnFxcVdX1/jx43k8XkBAAI/Hmz59urGxsa5rfBK1trbm5+fn5eVdvnz5ypUr9+7dMzY29vPzmzdvXkhIyPTp05lMpq5rBBgWEBQAhk5XV9f169f5fH5hYSGfz6+srGQymU5OTm5ubtOmTXN3d3dzc3Nzc8P4tjY0NDQUFhYWFhaWlZWVlpbeuHGDoihbW9uZM2cGBgZOmzbNz89PX19f12UCDDsICgA6U1tbm5+ff/0PlZWVhBBzc3Nvb283N7dJf3BycsLNgPtFLpff+kNFRcX169dLS0tlMhmLxZo0aZKXl5e3t/fUqVP9/PwsLCx0XSzAcIegADBciESikpKS69evX7t2raysrKKiQiAQEEL09fWdnZ3p0DBx4kRHR0cnJydHR8dx48bpumTdEwgEtbW1tbW1VVVV6nBQU1OjUqn09PQcHBxcXV09PDy8vLy8vLzc3d3HjBmj65IBRhgEBYDhSyQS0d+JKyoq6Bd37txpa2uj15qamjo4OEyYMMHBwcHR0dHOzs7a2trW1tbGxsba2prNZuu2+MHS0dHR0tLS2NgoEAhaWlpqa2tramroP2tqajo6OujNbG1tn3766UmTJrn2gFgA8PgQFABGGIlEUl1d3fNkSb9tamrq7OxUb2ZlZUUnBltbWwsLC7MezM3N6RempqZmZmY6iRSdnZ1CDdrb2+lMQOcDsVis/pSxsbGDgwMdjHqGJAcHBzzQGUBLEBQARo979+41NjaqT7H06baxsbG9vb3nmfi+TzEYDDMzMxaLZWJiYmBgYGRkZGRkZGBgYGJiQl8bYWho2POrOZPJ7DnrQVHUfW1KpVKFQkEIkcvlMplMJpPJ5XKxWNzd3S0UCpVKpUgkuq8GJpPZM8pYW1urUw79gsPh2NjYGBkZDe4RA4BHQlAAeOK0t7eLRCJ1bujq6mpvb+/u7haLxZ2dnR0dHfSZXiQSqVQqQgh9jld/XKFQSKVSuVze2tpqa2urp6c3bty4nj8mpNMGIURfX9/Y2JjOGXTsMDc3p3OGoaEhPZ5BMzExGfrjAAB9gaAAAAORlpYWGhra1tZmbm6u61oAQIvwmGkAAADQCEEBAAAANEJQAAAAAI0QFAAAAEAjBAUAAADQCEEBAAAANEJQAAAAAI0QFAAAAEAjBAUAAADQCEEBAAAANEJQAAAAAI0QFAAAAEAjBAUAAADQCEEBAAAANEJQAAAAAI0QFAAAAEAjBAUAAADQCEEBAAAANEJQAAAAAI0QFAAAAEAjBAUAAADQCEEBAAAANEJQAAAAAI1Yui4AAGDoiMViExOTvqwSCATp6ektLS0hISHu7u5DVSDAsIMRBQAYapmZmTwer6qqaig73bNnT3Bw8JQpU/qy6ubNmzExMUFBQVlZWT4+Pvfu3RvCSgGGFwQFABhq7e3ttbW1Uqm0Lxs3NjYOSqcxMTEqlUqpVPZl1fr16z08PBwcHA4ePPjDDz+MGzduUGroabD2C0DbEBQAYKhFRkbW19f3ZTxfKBSuWLFiUDplMpn29vZ9XHXu3DlTU1NCiKmp6WAV0NMg7heAtiEoAMAwJZVKlyxZMsQzFISQjo6OPo52DIyu9gtgYHAxIwAM3KZNm86ePZuWlrZ69eqcnJynn356586dPB6PXpuSknLhwgUDA4PS0tJp06Zt2LDBwMCAENLS0nLkyBFfX19/f//i4uJDhw6lpqaWlJSsXbv2+PHjLi4uP/30k4uLy7Fjx8rLy9vb22NjY7lc7rp1665du/bVV1+5ubk1NjbK5fJdu3Y9ssITJ0788ssv5ubmHR0d9432P3TVwYMH09PTCSHJycm3b99++umnP/jgg967eOhuHjlyJCYmxszMrLa2ViQS7dy589NPP/Xz87t8+fKD+zWAIw8wdCgAgP47f/48IYTL5TKZzHfffTcrKys1NdXS0tLIyKihoYGiqO3btwcGBioUCoqiWltbXV1dZ82apVKp+Hx+UFAQISQlJYWiqMbGxpCQEELImjVrSktLi4qKDAwMli1bRvcSHh4+YcIEdadcLpfP51MUJZPJgoKCHlnkoUOHeDxeR0cHXYO1tTWHw3nkqtbWVkLIpk2b+nIcNO0mRVGhoaH29vbqLT09PXk83kP3C2A4w9QDAAzc9OnTGQxGfHz87NmzFy1atGvXLplMlpCQIBAINmzY8NZbb7HZbEKIpaXl+vXrL168mJSUFBgYuGHDBnULHA7H19eXEPLZZ5+5ublNnTrV19e3sLDwwb66urpu3rxZVFRECDE0NHz77bd7r00mk61bty4uLm7MmDF0DXRA6X1Vv/Sym4QQIyOjnhsbGxsPoAsAnUNQAICB09PTY7FY9GmSEBIREaGvr19SUpKXlyeVSh0cHNRbhoeHE0Kys7PJA2dQJpNJCGGx/m8m1N7eXiwWP9gXm80ODQ1du3bt6tWrhULh8uXLe68tJyensbHR09NTvURfX/+Rq/ql990EGB0QFABg0LDZbDs7u+7u7urqakJIW1ubepWVlRU9K/E47R89enTp0qW7d+/mcrkXL17sfePy8nK6pH6t6hct7SbAsIKgAACDSaFQcLlcZ2dnQsidO3fuW8vlch+ncTabffjw4cTEREJIaGgofb7XhB4koM/lfV/VL1raTYBhBUEBAAaNQCBoamqKjIzk8XgmJibHjx9Xr6qvr5fJZPPnz+9Xg3p6ehKJhH4tl8t3795NCImKisrLy1OpVJmZmb181svLixCSnJysXqK+q1IvqwghFEX1sbzed5PFYkkkEnWzEolEpVI9uF8AwxyCAgA8FrlcXlJSQr/evHlzVFQUj8ezsrLasmXLpUuXMjIy6FU7d+6Mjo6eO3cuIaS5uZkQQv+4gBAiEokIId3d3fTb5uZm+scIhBA7O7vW1tbCwsILFy7IZLL9+/fT5117e3tTU1MfH59eCgsMDAwODj5w4EBCQoJMJrty5Qqfz29paTl8+LCPj4+mVTKZrKamhhAik8keue+976anp6dQKNyyZUtFRcWmTZvkcnlFRcXVq1cf3K/+HXGAocX89NNPdV0DAIw8d+7cSUxM9Pb2Lisrk8lkSUlJaWlpHA5n+/btDAaDEOLn5+ft7b1jx46CgoL8/HwLC4v4+HgGg5GdnR0fH19VVSUQCJydnaurq7dt2yYUCiUSiZ+f3/Hjx/fs2SMWixkMRlBQkJOT0+nTp0+ePMnj8Tw8PBITE0+dOlVfX5+UlBQdHb1gwYLei4yIiGhqatq3b19CQsLYsWNtbW29vLwCAgJcXV0XLVr00FUSiWT79u0lJSV1dXXW1taOjo70LyM00bSbhBAfH5/S0tKkpKTc3Ny4uDiBQMDlcp2cnFxdXR0dHdX75e3tPYj/XQAGHaPvg2wAAGppaWmhoaHR0dHJyckdHR26LgcAtAV3ZgSAEcze3l4ulz901Y8//hgWFjYiugAYzhAUAGDghEKhQqGQSCRjx47VSQF1dXWjoAuA4QwXMwLAwGVkZKhUqri4OD6fr+taAEArMKIAAANXV1dnbm6u6yoAQIswogAAAAAaISgAAACARggKAAAAoBGCAgAAAGiEoAAAAAAaISgAAACARggKAAAAoBGCAgAAAGiEoAAAAAAa4c6MANAnEonk7Nmz6relpaXGxsYnTpwwNjaml7DZ7IULF+qoOgDQFjxmGgD6RKlUPvXUU83NzZo2CAsLO3PmzFCWBABDAFMPANAnTCZz+fLlbDb7oWv19PRefvnlIS4JAIYARhQAoK8KCgr8/f0fusrAwKC1tVVXD5sGAO3BiAIA9JWfn5+Tk9ODy1ks1oIFC5ASAEYlBAUA6IdXXnnlwdkHpVKJeQeA0QpTDwDQD+Xl5VOmTLlvoYmJSUtLi4GBgU5KAgCtwogCAPTD5MmT3d3dGQyGegmbzV6yZAlSAsBohaAAAP3zyiuvMJlM9duurq4VK1bosB4A0CpMPQBA/9TW1jo5Oan/6bCysmpqauoZHQBgNMGIAgD0j4ODA4/H09PTI4Sw2ezo6GikBIBRDEEBAPotOjqavkyhq6trRTRndwAAIABJREFU+fLlui4HALQIUw8A0G8tLS22trZKpdLR0bG6ulrX5QCAFmFEAQD6zdraet68eYSQlStX6roWANAuBAUA6De5XD5hwgRCCIfDwagkwOiGqQcA6B+pVOrr61tRUUFRFEVRkZGRycnJui4KALQFIwoA0D8JCQm3b99WKpUqlYqiqJSUlAsXLui6KADQFpauCwCAkaG7u7u0tPS3335LSkpSqVTq5Ww2e9OmTc3Nzb6+vs7OzjqsEAC0AVMPAPBwKpXq5s2bv/3225UrV3777bfi4uKOjg4jI6Px48fX1dV1dXWpt3R2dq6pqVEqlVZWVr5/mD59OofD0WH9ADAoEBQA4P9raGgo/ENubm5bWxuLxZo0adK0P/j5+XV0dHh7ezc0NDAYDKVSGRIScvbs2e7u7uvXr/P5fPqz5eXlKpXK1tZW/cGAgAArKytd7x8A9BuCAsATrb29Pe8PV65caW9vZ7FY7u7u06dPnz59uq+vr5eX14PPlZZIJJ9//vmRI0f+8Y9/rFy58sE7M7a3t9PjEFeuXLly5Up9fT2DwZg0aZKvr29AQMCMGTM8PT1xP0eAEQFBAeDJolKpbty4kZeXl5ube/ny5fLycoqinn766YCAAHq+YOrUqYaGho9sJy0tLTQ0tK2tzdzc/JEbNzQ00KEhPz8/Pz//3r17Y8eO9fPzmzFjRkBAQEBAQF8aAQCdQFAAGP0kEsnly5dzc3Pz8vIuX74sEomMjY2nT58+Y8YMHo8XEBBgbW3d3zb7FRR6UqlUpaWldEy5fPlyRUUFg8GYPHlyQEBAYGBgQEDAlClT+lsMAGgPggLA6HTv3r2CgoL09HQ+n3/lyhWFQmFraztz5szAwMBp06b5+voaGBg8TvsDDgoPrZO+uCEnJ0ckEllbW/v7+9Ol+vv7PzjxAQBDCUEBYPRobGzk8/l8Pv/SpUtFRUUqlcrFxSUkJCQwMHD27NmOjo6D2NdgBYWeuru7r169mpOTc/HiRT6f39bWNm7cuJkzZwYFBc2aNWv69On6+vqD1RcA9BGCAsDIduvWrQsXLuTk5OTk5FRWVrLZ7OnTpwcFBQUFBc2cOdPMzExL/WojKPREz1Cod62xsdHQ0JDH482aNSsoKCggIMDIyEgb/QLAfRAUAEae5ubmixcvpqennz9/vqqqysjIyMfHhx6rnzVrlqmp6RDUoO2gcJ+GhoZLly7RMyllZWUsFsvb2zskJCQkJGTWrFkYaQDQHgQFgJGhra0tOzs7MzMzIyOjvLxcX1+fx+PNnTt33rx5OpnIH+Kg0FNtbW1WVlZGRkZGRkZ9fb2JiUlwcDB9KDw9PRkMxhDXAzC6ISgADF8ymSw3N5f+Gp2fn69SqSZPnjxz5syQkJDnnntu3LhxOqxNh0Ghpzt37qSnp6enp2dmZt69e9fa2nr27NmBgYEzZ86cNm2aDgsDGDUQFACGF4qirl69evbs2fPnz+fn5ysUiilTpsybN2/u3LmzZ88ePvcbGCZBQU2lUhUVFdEjLnw+XyqVTpgwYe7cuc8999yzzz47TIoEGIkQFACGhfb29rS0tLNnz549e7a5udnW1va5556bN2/evHnzbG1tdV3dQwy3oNCTQqHIy8vLzMxMS0vLz89nMBg8Hi8sLCwsLGzq1KmYmwDoFwQFAF0qLS09ffp0enr6hQsXVCrV1KlTw8PDX3zxxWeeeWaYn8+Gc1DoSSKRZGVlnT59+syZM3V1dfTcBH2Qh3nlAMMEggLAUJNKpZmZmSP91DVSgkJPIzeWAegQggLAEKmoqDh27NjZs2cvXbpEUZR6MNzHx2cknqVGYlBQU0/0/Prrr01NTba2tmFhYS+88EJYWBhuzwBwHwQFAO0qKio6duzY0aNHS0tLra2t6bNRaGjoSDy/9jSig4IaRVFFRUW//vrr2bNnL1++rK+vHxoaGhER8eKLL1pYWOi6OoBhAUEBYPDRV+CfOnXqP//5T0VFhYODQ1hYWHh4+PPPPz9qnlwwOoJCT3fv3v3ll1/oKaHOzk4ej7d48eLIyEh7e3tdlwagSwgKAINGqVRevnw5OTk5JSWloaHBxcUlPDx88eLFgYGBI3FyoXejLyioyWSyjIyM5OTkkydPikQiNze3xYsXL1myxM3NTdelAegAggLA4+ro6EhPT09OTj516pRQKKTPKy+++OLovuHPKA4KanK5PCcn59SpU8nJyY2NjaM7+QFogqAAMEByufzMmTOHDx+mR6oDAwMjIiL+H3tnHtfE8f//CSTct4QQblFAQEDkFvACUVS8QTyw1QLihdpPrUdt1VrPj61aD8RqFbzFG7XlEgWkoKACgoCIyE24EiABcu3vj/k2v3wQIkcOCPP8g0ey2Z157cyb2dfMzs7Onz/fxMRE0tLEwXAwCjw4HE5aWtrdu3fv3bv36dMnY2Njf3//JUuWjB8/XtLSEAiRg4wCAtE3OBxOcnLytWvX7ty509LSMnHixMDAwHnz5pFIJElLEyvDyijwk52dfefOnevXr5eWlpqbmy9ZsiQwMHDMmDGS1oVAiApkFBCI3pKVlXX58uWbN2/W1NQ4OjouWbJk8eLF+vr6ktYlGYatUeCRmZl57do1GA/29vZLlixZsmQJmvmIkD6QUUAgvkBdXd3ly5cvXrz49u1bCwsLeD0wNzeXtC4Jg4wChMPhPHv2DI4wUalULy+vr776asGCBYqKipKWhkAIB2QUEIjugbcYzp49e+/ePSUlpTlz5qxYscLLywvNYoMgo9AFJpMZFxd36dKle/fuKSoqzp07FwUMQjqQkbQABGLQUVRU9O233+rq6k6fPp1Go0VFRdXW1kZHR3t7e6NGH9ETcnJyfn5+N2/erKys3LNnT15e3rRp08zNzffv319bWytpdQhE/0EjCgjE/8FkMu/evRsZGfn06VNjY+Pg4OAVK1YYGhpKWtcgBY0ofJGcnJyLFy9eunSppaXFz88vNDR02rRpMjKoe4YYYiCjgECAysrKc+fORURENDQ0TJ06NTQ0dP78+Xg8XtK6+kxra6uqqirvK4VCSUxMrK+v9/b2tra2Fm5eQ9QoiLOIIJ2dnQ8ePDh79mxSUpKent7y5cvXrVuHDGhvEH9lIboHQyCGKxwOJyEhwd/fX1ZWlkwmb926taysbODJJiUlubi4fPz4ceBJ9Z4zZ85MnDhRX1+ft6WwsNDPz6+8vHzu3LkEAoFGo/V0bP8Ex8fHAwCampr6oXaYFFEXioqKtm7dSiQSZWVlvb29b968yWazB5KgeBielYXgBw2CIYYjNTU1u3btMjIymj59eltb2+3btysqKg4ePGhsbDzwxJubmysqKuh0ei+VDDxHAEBwcDCXy+VwOLwtO3bsGDt2rKGhYVRU1MWLF9XU1Ho6tk+ChcLwLCJzc/ODBw+Wl5dHRUUxmcyAgAAzM7P9+/dTKJSBJCtqhmdlIf4HSTsVBEKsZGZmLl26VE5OjkQibd++vbS0VIJimpubJ0+eLKzUAgMDdXV1eV+VlZUPHjworMQ/ZyAjCr1nSBeRYAoKCjZv3qylpSUvL79ixYqXL19KSomwkOLKGuagEQXEsIDD4cTGxk6bNs3FxaWgoODEiRMfP37cv3//yJEjJSWJTqcHBASUlZWJIvH29nYp6FFJdxFZWlr+9ttv1dXVZ8+ezcnJcXJycnR0PHv2bEdHhwRV9RvprqxhDjIKCCmHSqUeP37c1NR03rx5CgoKCQkJr1+/Dg0N/eJ6OAUFBT/88IOVlVVVVdXcuXO1tLScnZ0zMjJ4O9y6dWvDhg3fffedr6/vzp07Ozs74fb6+vqTJ09mZmYCAN68ebNlyxZTU1M6nR4cHKytre3s7FxaWgoAuHv3bmFhYUNDQ0hIyJEjRwAAOTk5X3/99eHDhzdv3rx27drenN39+/dDQ0O3bt0aHh7OG/WNiooKDQ0FAMTExISEhBw6dEhwIr0U3C2//PILKiLBRSQYOJzw5s2brKwsKyur9evXm5iYbNu2rbKysn8J9gSK54FX1vBF0kMaCISoKCwsDA8PV1JSUlNTCw8P7+vkppSUFCsrK1lZ2U2bNiUnJ9++fXvEiBFKSkrV1dUYhv3222/u7u5MJhPDsIaGBjMzs4kTJ3K53LS0NE9PTwDArVu3MAyrqanx9vYGAKxbty4/P//169fy8vKBgYEwi9mzZ5uYmPBytLCwSEtLwzCMwWB4enp+UeGVK1dcXV3b29uhBiKRyBuqbWhoAAD88ssvX0ykT4L5gbceLCwsUBH1VET9oLq6eteuXUQiUU5Ozt/fPyEhQSjJYiieRVBZwwdkFBDSBpfL/euvv6ZOnQoAsLS0jIiIaGtr619SK1euxOPxsPXEMOzGjRsAgJ9++qmurk5ZWfnSpUu8PS9cuAAAiI6Oxv69gsJ2CsOw7du3AwAaGhrgVw8PDzMzM/iZv2FlMpkAgBMnTsCvV69eFayNTqeTyWT+3RYsWNCPhrVPgj8/atmyZaiIeiqiftPe3n7+/Hl7e3sAgIuLy40bN1gs1sCTRfGMiaCyhgPo1gNCemAymdHR0XZ2dr6+vng8Pi4uLj8/PywsTFlZuX8JysrK4vF4AoEAv86fP19OTi4vLy8jI4NOp/M/Cj979mwAwNOnTwEASkpKXRIBAPBWZTAwMGhtbf08LwKB4OPjs3HjxrVr11Kp1CVLlgjWlpqaWlNTY2Njw9siJyfX91ME/RYMkZGRQUUkuIj6gYKCwqpVq169epWammpoaLh06VIzM7Njx44NMBcUzwIEIwSAjAJCGmhtbT1+/Pjo0aNXrlxpbGz84sWLuLg4Hx8f4a64TCAQ9PT02Gz2p0+fAABNTU28n7S1teEo7kDSv3PnzuLFiyMiIiwsLFJSUgTvXFhYCCUNJEehg4pIuHh4eMTExLx//37x4sW7d+/W19ffuHFjeXm5UBJHlYXoJcgoIIY2dXV1u3fvNjY23rlz5/z58z9+/BgbG+vk5CSi7JhMpoWFBXxW4vMpURYWFgNJnEAgXL169dKlSwAAHx8f2HT2BOxvwSZ+UIGKSOiMHDkSLsCwd+/eu3fvjh49OiAgAE7WGyCoshC9ARkFxFDl/fv3GzduNDExOXPmTHh4+KdPn44fP25kZCS6HCkUSm1t7cKFC11dXVVVVe/du8f7qaqqisFgzJkzp08JysjItLW1wc+dnZ0REREAgOXLl2dkZHC53CdPngg41tbWFgAQExPD28K/QA0moaXZURGJDjU1tY0bN5aUlFy5cqWsrMzV1dXDwyM2NrbfJ4IqC9FLkFFADD1ev34dEBBgYWHx+PHjgwcPlpWV7d69W0tLSxR5dXZ25uXlwc/79u1bvny5q6urtrb2gQMHnj9/npSUBH/6/fffg4KC4AzKuro6AACcfgUAoNFoAAA2mw2/1tXVwXndAAA9Pb2Ghobs7Oxnz54xGIzz58/DltHAwEBdXR3OZesJd3f3SZMmXbhw4cyZMwwG4+XLl2lpafX19VevXmUwGHB0msFg9OYcey8YFVH/ikiIwKchMjMz4+LilJWV586da2Njc+nSJRaL1ZvDUWWJs7KkBtndu3dLWgMC0VuePHkSEhKybds2NTW106dPnzhxwtXVVXRvb4qNjc3Ly2MwGJcvX05ISNDV1f3tt9/gvAdnZ2c7O7vjx4+/ePEiMzNTS0vr8OHDOBzu6dOnhw8fLisro1AoI0eO/PTp05EjR6hUaltbm7Oz87179yIjI1tbW3E4nKenp7Gx8cOHDx88eODq6jp27NhLly7FxsZWVVVdvnw5KCho7ty5guXNnz+/trb2jz/+OHPmjIqKCplMtrW1dXNza2tr++233/Ly8iorK4lEopGRkYKCQk+J9Ekw/5sPS0tLL126ZGdnV1BQgIqo2yISHTgcbtSoUUFBQQsWLCgtLd23b9/FixfxePzYsWMF3OZH8SyRypIGJPW4BQLRJxISEtzc3AAA7u7uDx48EE+mwcHBCgoK4slryAEfQgsKCkJFJHHKysrgkiHa2tq7du1qbGzsdjcUz4j+MfRepIsYVnC53EePHv38889ZWVne3t7p6enQLgwTDAwMeAvkdSE6OtrX11fM6QxCUBEBAIyNjY8fP75z585Tp06dOHHi119/XbVq1ZYtWwwMDCQt7X9AlTVEQUYBMUhhsVjXrl07cOBAcXHxzJkzX7586ejoKGYNTU1NTCazra1NRUVFzFlDhLWOr9DXA+ZBpVJREQ0SiETi7t27v/vuu/Pnz//3v/89c+bM4sWLf/jhB97DCyieEf0D3aFBDDpYLFZ0dLSVlVVwcLCTk1N+fn5sbKz4XcL27dvj4uK4XG54eHhaWpqYcx8qJCUloSIaVKioqMCHI37//ff09HRra+ulS5fm5+ejeEb0GxyGJn8iBg1MJvPPP/88cOBAXV3dqlWrtm3bJtLHHREDISEhwcfHp6mpSVNTU9JaEN3D4XBu3ry5f//+goKC+fPn79y5c9y4cZIWhRh6oBEFxKAArr5saWm5cePGGTNmlJSUnD59GrkEBGIgyMrKLlmyJDc39969e+Xl5fb29tOmTRPKSk2IYQUyCggJw2Qyz549O2rUqJCQEG9v7w8fPkRGRg62SVgIxNAFh8P5+fm9ePEiISGhtbUVrtQkePkjBIIfZBQQEoNnETZs2DBz5szS0lJkERAI0eHt7Z2RkZGamiovL+/l5QUXdpS0KMQQABkFhARgMpmnT58eOXLkpk2bFi5cWFZWFhkZqa+vL2ldCIT04+HhkZSU9OTJEzk5uTlz5nh6evIWZEQgugUZBYRYYbPZf/75p7m5+X/+8x9/f//S0tJjx46RyWRJ60IghhdTpkx58uRJamqqoqKit7f3pEmT4EulEYjPQUYBISa4XG5MTIy1tXVYWNjEiRMLCgqOHTumq6sraV0IxPDFw8MjPj4+PT1dTU1typQpcLBB0qIQgw5kFBAiB8Ow2NhYBweHwMBA+GqA6Oho+GZbBAIhcdzc3GJjY58/f66pqent7e3h4ZGcnCxpUYhBBDIKCNGSmJjo5OQ0d+5cMzOzgoKCmzdvjh49WtKiEAhEVyZMmBAbG5uamqqgoDB16lQPDw90MwIBQUYBISqePXs2YcIEHx8fQ0PDnJycmzdv8paSRSAQgxMPD4/ExMTExEQAwJQpU3x9fbOysiQtCiFhkFFACJ/c3NxZs2ZNnjxZVVX1xYsXd+/etbGxkbQoxEBhMpmlfNTU1AAAysrK+DdKWiNCOHh5eaWlpcXFxVGpVGdn50WLFr17907SohASAy3hjBAmFRUVv/zyy/nz5+3t7Q8ePOjl5SVpRQihwWAwdHR06HR6Tzs4OzujVf+kj8TExO+//z4nJ2fhwoUHDhwYNWqUpBUhxA0aUUAIh8bGxm3btpmbm8fFxZ0+fTozMxO5BClDSUlp3rx5srKy3f4qIyMTGBgoZkkIMeDt7Z2VlXX9+vXXr19bWlquXr0aDiYhhg9oRAExUOh0+smTJw8cOEAgEL777rtNmzbJy8tLWhRCJDx+/HjWrFnd/oTD4SoqKtCqWVIMi8W6cOHCnj17aDTa+vXrt23bpqGhIWlRCHGAjAKi/8CGY/fu3a2trevWrduxY4eampqkRSFECJvN1tHRaW5u7rJdRkbG09MTTZIfDjAYjD/++GP//v1sNnvDhg3/+c9/VFVVJS0KIVrQrQdEj6Smpp45c6bbnzAMg6snrV+/3s/Pr6Sk5ODBg8glSD14PH7x4sVycnJdtuNwuKCgIIlIQogZJSWljRs3fvjw4fvvvz969OioUaMOHTrU2dnZ7c5cLvfgwYOoOzrkwRCI7igoKFBTU1NTU2tqauryU0JCgoODg4yMjL+/f0lJiUTkISTFs2fPPm9G8Hh8Y2OjpKUhxE19ff3WrVsVFBSMjY0jIyPZbHaXHaKiogAA33//vUTkIYQFMgqIbqiurtbT08Pj8Xg8/ttvv+Vtz8vL8/f3BwB4e3u/evVKggoRkoLL5erp6XVxCX5+fpLWhZAY5eXloaGheDzeysrq5s2bXC4Xbm9vbyeTyTgcDgBw9OhRyYpEDAR06wHRldbWVh8fn/r6ejabzWazf//99/fv33/69Gn16tV2dnafPn168uRJQkKCvb29pJUiJAAOh1u2bBn/3QcOh7N8+XIJSkJIFkNDw8jIyLy8PGtr68WLF7u5ucEXRhw/fpxCoWAYBgD49ttvo6OjJa0U0U/QZEbE/8BisXx9fZ89e8Zms+EWAoFgZ2eXk5MzatSoffv2zZ8/H3YREMOW169fjx8/nvdVQUGhoaFBWVlZgpIQg4SsrKwffvghPj5+8uTJWVlZbW1tvJ9kZWUfPXo0ffp0CcpD9A80ooD4/2AYFhwczO8SAAAsFisrK2vz5s15eXkLFixALgFhb2/Pe2EHgUBYtGgRcgkIiKOjY1xcXHJyckVFRZcZjhiGzZ8/Pzs7W1LaEP0GGQXE/2fnzp2XL1/mdwkQPB6fkJDQ00o7iGFIUFAQgUAAALBYrKVLl0paDmJwYWJiUl5ezmKx+DdyuVwmk+nt7V1cXCwpYYj+gYwC4v+Az0ZzudzPf2Kz2W/evLlx44b4VSEGJ0uWLIGXAXV1dW9vb0nLQQwutm/f3u12DofT1tbm7e1dW1srZkmIgYCMAgIAAGJjY8PCwgTv891333V0dIhHD2KQY2ZmNm7cOADAsmXL4NACAgF59erVjRs3ugwn8GCz2bW1tdOnT29tbRWzMES/QZMZhUlHRweVSqVSqc3NzXQ6vbW1lc1mNzc3s1istra29vb2jo6OlpYWDofD5XJpNBr/sfBX/i0qKir8TbCMjIy6ujoAQF5eXklJSVlZWU5OTkNDA4/Hq6mpKSgoKCoqqqura2hoaGhoaGpq4vH4XsrOzMycNGkSi8XqdjhBTk6Ow+FwOBwcDvfHH3988803fS4XxKCByWS2tLS0tLQ0Nze3tLSw2WwqlQqjEf7lcDhwO2zH4dcuicCdy8vLi4uLHR0dNTQ05OTkPp+moKmpyfuAw+E0NDTgXxjJsrKyampq8vLyampqqqqqmpqaampq6PaWFODt7Z2UlMS7M9XtPgQCwcPD4++///588a6e4HK5vNaVSqV2dnbS6XQGg9HZ2cmLZNjSAgDgdv7DYdDyb+HFJ0+SiooKAEBJSQmGJR6P19DQgNsVFRUVFBQ0/kVdXX1Y+WNkFHoFh8Opr6+nUCjV1dUUCqWurq6mpqa+vr6xsZH6L83NzZ93uGHLiMfjVVVV4bWcd/lXV1eXkfn/Izq8MOVBpVL5a4fJZMIX93V0dLS3t7e1tbFYLCqVymazP2/KAQAqKio806ChoaGlpaWrq6urq6ujo0Mmk3V0dEgkkra2dklJibOzM++/CNoLOE1BXV3dxsZm/PjxY8eOtbW1tbKyQmu1Dk7odHp9fX1dXV3Dv1AolPr6eiqV2tLSQqVSaTQa9AfdjgnBK/fnf+Gv8ALPvz9sSRkMxv379wMDA3E43OftMq/JxjAMRnKXv92eiLKystq/QNerpaWlo6Oj/S8waLW1tRUUFIRTdghhU1FR8e7du4KCgoKCgtzc3IKCAmg6CQQCDodjMplwNxwOFxAQcPXqVRwOV19fDwMYtqu1tbW1tbVNTU1UProdgeBd1GVlZWHvCLZRnztX2LPifeVZYR68rhqdTmcymdA0wwa226yVlZU1+ICBqqenRyQSSSQSbGmJRKJ0eF9kFP6Hmpqacj7KysrKy8th7PLcqIKCAgwIHR0dLS0teBnmXY95n2GTJ7Yo6ezsZDAYNBqN33Tzf2hsbKypqamrq6NQKPyPPmIYBr/i8XgSiWRlZWVra+vu7j5hwgQSiSQe8YgvQqfTYShWVlZWVVXBQK2pqampqWloaGhvb+ftqaioqK2traOjo6Ojo6GhoaamBjtAvKsv/AA34vF4niHoB3/99Zevr2//joV2ocsIBz8wmKHvqa+vb2ho4O+eqqioQMtrYGBAJpMNDQ319PQMDAz09PT09fXRa8kGFTU1Nfn5+ZmZmdnZ2fn5+eXl5TzDqqys3NnZyd8iwQstmUzW0tLib1H5r8qamppwYFVspwC7Z7wGlh/YxjY1NfGMDs80y8jIEIlEXV1dIyMjY2NjIyMjQ0NDQ0NDExMTMpnM31Ec5Axfo1BdXV1UVFRcXFxUVFRYWFhSUlJeXg4rWEZGhkwmm5iYGBkZGRkZ6enpwcCFDdNAGtZBAoVCgeMi58+fp1KpsrKyLBarsbGxvLycQqHAfVRUVEaOHGlubm5ubm7xL10G6xBCp6Ojo7S0tLS09MOHD6X/UllZyRs0UlBQ0NPTgxdFMpmsr69PJBL5e9tS/KQijUbjHzWB/c7KysqampqKioq6ujre9YZIJBoYGJiamo4aNcrU1BR+MDIy6v39OES/wTCsoqKi+F+KiopKSkp4T0vKyMjo6urq6empqKjg8fj29vapU6dOmTIFWlsikShp+UKgubm5trYWDo3U1dVVV1dXVFR8+vSpvLy8uroaRimBQNDX1x89ejSvjTU3Nzc2Nh6cIxDDxSjU1dXl5OS8efMmNze3qKioqKgIjiapq6vzKsnExASaPn19/WF1/4mf9vZ2GNDl5eUfP37k/bfDHgCRSLSwsBgzZoydnd24ceNsbW3Ri6D6DZfL/fTpU0FBQX5+PrSqpaWl1dXV8F+SRCLxrnOGhoaw00wmk6WjJRUFXC63trYWDrdUVFRUVFRAs/Xhwwc4HwiPxxsZGZmamo4ePdra2trKysrKykpXV1fSwoc8Hz9+zM3NzcnJefv2LWwu4BCXlpYWvAqamZkZGxsbGxsbGhrq6+v3fl6C9MHhcKqrq8vLy2EzW1JSAr0U7KHJycmNHj3awsLCysrKzs7Ozs5u9OjRg2HgQWqNwvv377OysqA5yMnJgU/jGBgYwHteDDoLAAAgAElEQVTtMHzHjBmDRtd7A5y5BgdgCgsLCwsL37x509TUhMPhRo4cOW7cOBjTzs7OZDJZ0mIHL+Xl5W/evCn4l3fv3jEYDACAgYGBpaWlmZkZfw+4y4QVxEBobGzkDdJ8+PChuLi4oKCgqakJAKClpWX1L9bW1vb29iNGjJC03kENi8XKycl5/fp1Tk5Obm5ubm4ujUbD4XCmpqZ2dnb8/WNtbW1Jix0yUKlU3gBMcXFxXl5ecXExh8NRVlYeO3YsbGDHjRs3fvx4iczOkR6jwGKxcnNz09LSnj9//uzZMwqFgsfjzc3NYdfBwcHB2dkZ2QIhUl1dDXvD2dnZ2dnZhYWFXC6XTCZ7eHi4u7vDAh/OXQcAQHV1dfa/vHz5sq6uDgBAJpOtra1NTU3hlcnW1lZHR0fSSocjzc3N+fn5MIYLCgrgXR4AAJlMdvgXV1dXNIQDAKDRaC9fvoSta3p6OoPBUFFRgR1fBwcHZLBEAYvFKi4uzs7OhiH64sUL3kWN18ZaW1uLR8zQNgpMJvP58+fx8fFPnz599eoVk8kkk8lubm7u7u5ubm7jx49Hc5rERktLS2ZmZnp6enp6ekZGRktLi6qqqouLi5eXl4+Pz7hx4wbDAJqoYbFYL1++TE1NTUtLy8rKqq2txeFwZmZmjo6ODg4Ojo6O9vb26MmRQUtdXR3P2GVlZVVVVQEATE1NnZ2dPTw8Jk2aZG1tPXyWMK+trU1MTExMTExLS/vw4YOMjIylpaWbm9uECRNcXV3HjBkzfIpikFBaWvrPv+Tm5rLZbH19fQ8PDy8vr2nTppmYmIgu6yFpFN69excfHx8fH//s2TM6nW5mZubl5QUn6puamkpaHQJwudz8/Pznz5+npaUlJibW1dURiURvb28fHx8fH58uLyke6jAYjMzMzJSUlJSUlIyMDAaDoaur6+np6ezsDHulaBrHEKW2thY6hszMzOfPn7e0tIwYMQI6Bk9PT3t7+8E572wg0On0lJSUxMTEhISEt2/fEggENze3SZMmubm5ubq6amhoSFog4v+g0+kvX75MT09PTU1NSUlhMBhmZmbe3t7Tpk2bMmWK0GtqKBmF/Pz8mJiYa9euFRcXq6iouLq6zp49e86cOSNHjpS0NIQgSktLYdfkr7/+amtrs7Ky8vf3X7p0qbm5uaSl9Z+ysrL4+PjY2NiEhITOzk7ePRcPD4/x48ejzpaUweFwCgsLnz9/npiYmJycDN+WOWXKFD8/Pz8/v6E+NYdKpSYkJMTGxt65c4dOp5uamnp7e0NnLwUPeUk9bDY7JycHtrFPnz7FMMzV1dXf3z8gIEBYkTkEjMKrV69u3rwZExNTWlpqamrq7+8/f/58R0dH6bPzUk97e3tKSsrt27fv3r3b0NDg6OgIo1mkg2ZChMPhPH/+/NGjRw8fPiwoKFBXV/fx8Zk1a9bUqVMNDQ0lrQ4hJjAMKygoSExMfPjwYUpKCofDcXFxmT179uzZs21sbCStrg80Nzffv3//1q1bCQkJAABvb++FCxf6+voOdd8znGlsbExISLh9+/bjx487Ozs9PT0XLVq0cOHCgT7dgw1W2tvbo6Ki7O3tAQBGRkbh4eGpqalcLlfSuhBCgM1mp6amhoeHk0gkHA7n7e198+ZNJpMpaV098vbt2127dkFDY2pqGhoa+uDBg87OTknrQkgYOp3+4MGD0NBQfX19AICVldWuXbtKS0slresLZGVlhYaGwmUNZ8+eHRkZSaFQJC0KIUza29sfPHgQFBQE1/2DbSybze5faoPRKOTl5a1du1ZNTU1RUfGrr75KT0+XtCKEqGCxWPfv3/f19ZWRkdHX19+zZw9cSGCQUF1dfeTIEVtbWwDAyJEjf/zxx8LCQkmLQgxG4GjTmjVrtLS0ZGRkvLy8Ll682NraKmld/wOVSj1x4sTYsWMBAOPHj4+MjKTRaJIWhRAt7e3tV69enTRpEuzkHDhwoK6urq+JDC6jUFBQEBQUJCMjY2ZmdvDgwfr6ekkrQoiJysrKXbt26ejoyMnJhYaGStwuvH//Pjw8XEFBQV1dPSgoKCEhAY1mIXpDZ2cn7MkpKSmpqamFh4dXVlZKWhTW2tp68OBBTU1NBQUFf3//hIQESStCiJuioqKtW7dqa2vLy8uHhobW1NT0/tjBYhQ+fPgQFBQkKytra2t779491CgPTzo6Ok6ePKmnp6esrLx9+/ampibxa0hJSZk9ezYOh7O0tDx//nxHR4f4NSCkgIaGhl9++YVEIikoKISEhBQVFUlERltb26FDh7S1tdXU1Hbt2tXc3CwRGYhBAoPBOHr0KIlEUlFR2b59e2NjY2+OkrxRYLFYBw8elJeXNzExiYyM7PdNFITU0NnZGRkZSSKRNDQ0oqOjxZZvSUnJrFmzAADu7u4DuZ+HQPDo7OyMioqytLSUkZEJCgoS8yhpXFycoaGhsrJyeHh4PwacEdIKnU4/duyYrq6uhoZGZGTkF/eXsFF49+6di4uLgoLCkSNHWCyWSPNqaWkRafoiZbCJ76Knrq7uypUrx44de/v2rRCzCAsLw+FwgYGBDQ0Nwkq2WxgMxo8//qigoGBjYwOfLxIDg61O+8SQEy9ZwRwOJzo6WldXl0gk/vnnn2IYMaXRaMHBwTgcbvHixaK2CEMuGPgZ0uIHSEtLy/r163E43Lx58wTfiZCkUbh06ZKioqKzs3NBQYFIMzpz5szEiRP19fXh16SkJBcXl48fP4o0U2Fx8uRJDw8PKysrSQv5P7oUJoZhhYWFfn5+5eXlc+fOJRAIwp0eFRcXB1+TmJmZKcRk+Xnx4oWpqamamtrRo0dF7VYhKCDFyeARTKVSw8PD8Xj8xIkTq6qqRJfRmzdvjI2NdXR0YmJiRJcLhiJZKkhOTjY1NR0xYkRSUlJP+0jMKBw5cgSHw33//fdiaJrZbLaHh4euri78euvWLT09vS/2fSU+nw7CYrFsbGzGjBkjaSH/R5fCxDBswYIF27dvxzCMSqVeuXJF6Dk2NzfPmjVLSUkpLi5O6IlHR0fLy8tPnz5dnNWNAlKcDDbBr1+/trCw0NXVffnypSjSf/Lkiaqq6tSpU8XwxCOKZOmgtbU1MDBQTk7u+vXr3e4gGaPw559/4nC4Y8eOiS3HwMBA/mvbF2lubp48ebLo9PSJGTNmDKpo7lKYysrKBw8eFGmOLBYrKChIWVlZuOMK58+fl5GR2bJlC4fDEWKyvQEFpDgZbIJpNJqvr6+amto///wj3JSzs7NVVVUDAgLEtsgHimTpgMvlbtq0iUAgPH78+PNfJfCenrdv365du3b79u0bN24Uf+69gU6nBwQElJWVSVrIEKC9vZ1Op4s6Fzwe/+eff06cOHHx4sU0Gk0oaT59+jQsLGzHjh2HDx8e5C+sQgEpZaipqd27d8/T03PevHmVlZXCSralpSUgIMDFxeXy5cuD88WtKJIHLTgc7rfffluyZMny5csrKiq6/CqB9nHTpk329vY///yzqDO6f/9+aGjo1q1bw8PDa2pqeNvr6+tPnjyZmZkJv+bk5Hz99deHDx/evHnz2rVrAQB3794tLCxsaGgICQk5cuQIAKCuri4kJGTv3r0hISHz589vbGwEALx582bLli2mpqZ0Oj04OFhbW9vZ2Rm+qRby+PHjtWvXbty40c3N7Y8//oAbMQw7c+bMmjVrXFxcfHx83r9/38vTSUpK8vHx0dLSmj59emlp6f3791VVVeHADJPJBAD8888/ZDJ5//79X0zqc2E3btxQVVWF6xDTaLS9e/fKysq6ubkJLsyoqKjQ0FAAQExMTEhIyKFDh3p5Lv0Aj8dHRUXR6fSDBw8OPLWOjo5Vq1bNmTNHDHHIAwXk5wjQLCAm8/Pzd+zYYWFhUVlZuXv3biMjI2tr6+Tk5I6Ojs2bN48aNcrIyCguLk6wYLix25IUA3JycteuXdPQ0NiwYYOw0jx48CCNRrt8+TKBQBBWmt2CIrkn+tS0FhQU/PDDD1ZWVlVVVXPnztXS0nJ2ds7IyBiIgIGAw+EiIiJGjBixbdu2rr+JeXzjzZs3AIDk5GRRZ3TlyhVXV9f29nYMwxoaGohEIhwfS0tL8/T0BADcunUL7mlhYZGWloZhGIPB8PT0hBtnz55tYmLCS23y5MmLFy+Gn+3s7JYvX45hWE1Njbe3NwBg3bp1+fn5r1+/lpeXDwwMhLtFR0cvWbIEjmnv27cPAACnihw4cODixYsYhrHZbCsrK11dXTqdLvhcZsyYMWLEiFWrVv3999+nT59WUlLS09Nra2uD1cm709nZ2eni4vLFkulJmI+Pj4GBAW83GxsbV1dXwYUJvwIAfvnlly/mKxQOHz6sqqoKlQyEs2fPKikpiXRCWRdQQHaLYM09xSSFQgkKCgIAhIaGZmdnt7S0uLi4mJqarl+/vqCgoLW1Fb5I9ouCeypJsfH3338DAF6/fj3wpNrb21VVVQ8fPjzwpASDIrkn+tq0pqSkWFlZycrKbtq0KTk5+fbt2yNGjFBSUqquru6fAKFw48YNGRmZiooK/o3iNgr79u0zMDAQ9dNBdDqdTCZfvXqVt2XBggW8a1t8fDwvmqFfO3HiBPyJd8jn0bx//374edmyZba2tvDz9u3bAQC8h/c8PDzMzMwwDKNQKOrq6rwl3ykUyoIFCwoKCqqqqkgkEu+O+E8//QQA6Gn+CI8ZM2bo6enxvu7cuRMAcPz48YqKCjweHxwcDLc/fPhw7969gpPqSRiGYfPmzeOPZldXVxjNggtTzEahqqoKh8PFx8cPMJ3p06fz2h0xgAJSAD1pxnqOSQzDTp06BQDIzc2FX3ft2sV/xf3xxx8BALzZfD0JxnouSfHA5XJNTU137tw58KTi4uJwOJyo5wmiSO6JfjStGIatXLkSj8fzXnNz48YNAMBPP/3Uv38lodDZ2ammptZlcQVx33r4+PGjlZWVqF/Cm5qaWlNTw/8mN/47dkpKSrzPBALBx8dn48aNa9eupVKpS5Ys6TbB5OTk7du3t7e3nzt37sWLFwwGA26Hb7DE4/Hwq4GBQWtrKwAgLS2Ny+Xy3n9NJBJv375taWmZnp7OYrFWr14dEhISEhJSXV0dHBysqKj4xTNSU1PjfV65ciUAIDs728DAwN/f//Lly/BqffPmzaVLlwpOpydhAg4RXJhiRk9PT1NT88OHDwNMp7i4eNy4cUKR1BtQQAqgJ829OYo3ucTAwAAAwBtyNzIyAgBAGQIEg55LUjzgcLjx48cXFxcPPKmPHz9qamqK+sWPKJJ7oh9NKzxNPB7Pi9v58+fLycnl5eX1719JKMjJyZmZmXVpY8VtFGRlZTkcjqhzKSwsBHythmDu3LmzePHiiIgICwuLlJSUbvfhcDgHDhz46quvzM3NXVxcvpjm27dv4WOfXba/e/dOWVn5j/9lzpw5vdHJw8TERE5Orr29HQCwefPmjo6Os2fPMpnMhoYGU1PT/gkTQJ8KUwyw2eyBi8Hj8Ww2Wyh6egMKSFHTpe8Bv3K53G535hfc15IUOiwWSyj/XKhpHXJN6+cQCAQ9PT3YNEnqXwl018aK2yhYWFjk5OSIOqChyf306VNvdiYQCFevXr106RIAwMfHB/4n8MPlcmfOnJmXl3fz5s2JEyf2Jk01NbWOjo6CggL+jZ2dnUpKSpWVlV3mOdfX1/cmTR4yMjJ4PB6+As7Jycnd3f3UqVMPHz708/PrtzABh/SpMEVNSUlJS0uLubn5ANOxsrL6559/hCKpN6CAHFTwBPejJIULh8N58eKFtbX1wJOysLCg0WgDH2wTDIrkvgrrkwAAAJPJtLCw6IcAYdHW1lZUVAQ18BC3UZgzZ05TU9ODBw9Emgt8L3BMTAxvC5fL7daddHZ2RkREAACWL1+ekZHB5XKfPHkCAJCRkWlra4P7vHjxIj4+3svLC37tjW10dHQEAPz444+8Pk12dva1a9dsbGwwDNu6dStvzw8fPpw+fbpPZ1dWVsZisQICAuDX7777rrq6+j//+Y+/v/8Xj+1JGAAAj8e3tbXxSqmtrQ3uI7gwB+ig+8qff/5JIpH4H8foH0uWLImLi3v37p1QVH0RFJD9o6eYHCA8wf0oSeFy/fp1CoUy8IICALi5uZFIpHPnzg08KQGgSO6rMNCXMKZQKLW1tQsXLuyHAGFx+fJlDMN8fX3/Z6t45kfw4+/vb2Fh8cX5qANk0qRJsrKyERERdDr9xYsXenp6AIArV67Q6fTbt28DAM6cOYNhWEdHh42NDXz9D5PJ1NbWTk9PxzAsLCwMAJCVlfX06VMY356enrm5uRcuXLCxsVFRUcnJyamtrYWPNvFm3EyZMkVdXR1O1YQFPWXKlJMnT27ZssXPz4/FYnG5XCcnJwDAggULLl26dOrUKS8vry++J2bWrFkkEglO0uZyuatWreKfPMhmsw0NDefOndvLkulWGIZhe/bsAQDs3bu3qKho7969ZmZmGhoa2dnZggsT3ujdsWNHH+unP7x//15ZWfm///3vwJPicDj29vbu7u5iW5cGBWRPCNAsICYPHz4MAHjz5g086tdffwV8j1MdPXoUAAD3FCA4IyOjp5LspfiBUFVVpaurGxISIqwEjxw5oqSkJOrXVKJI7ol+NK3wfRy8Obnh4eErVqzot4CBQ6FQdHV1w8PDu2yXgFGoqKjQ0tIKCgoS6bMPVCp15cqVJBLJyMho9+7doaGhK1euTExMTEpKmjx5MgDA2dk5ISGho6PDyclp1qxZhw8fDg0NPXv2LDw8JyfHwMDA3NwcLpYeFhamqqrq6uqamJj46NEjbW3tRYsWxcbGwqkra9eupVAoly9fVlZWBgDs3r2bzWbT6fQ1a9bo6+uTSKQ1a9ZQqVSYcmNj47Jly3R0dIhE4ooVK3rzhF5OTk5gYOCMGTNCQ0M3btzIe/qIx+rVq3u/qHtPwmg0mp+fn4qKiqur68uXL1etWvX1118/evRIQGFmZWXBKTYjR468cuUKLylR0NLS4uDgMH78eGFd2t++fauqqhoUFCSet0SigOyWJ0+eCNDcU0w+efIEdm2XLVtWUlLy7NkzODV15syZubm5z58/Hz9+PPz1w4cPggV3W5LwyiFSmpqaHB0dx4wZI8T/ms7OTicnJ1tbW5H+J6JI7ol+NK3BwcEEAmHlypWLFi0KDg7es2dPl1Vi+yRggHR2dnp7e5uYmHweP5JZwvnvv/+Wk5Nbv369+JfOlUqcnJwGvrTAYIZGo02ePFlXV7ekpESIycbFxSkqKornwjCskPqAHCBlZWV2dnZGRkbv378XbsofP37U19d3d3dvamoSbsrDE1FHcnBwsIKCggQF8GAwGHPnzlVTU8vKyvr8V7wwbmr0menTp1+9enXZsmXV1dXnz5/X0NCQiIzBg4GBQU/TXqKjo7veLvpfnjx5MnXqVAUFBaGkNggpKSlZtGgRhUKJj48fNWqUEFP28fH5+++/Fy1a5OLiEh0dDbuhCIACUpTcuXMnNDRUX18/JSXF2NhYuImbmJgkJCRMnz59woQJt27dEso0ySHNkI7kzwWIiPLy8sDAwMLCwkePHjk4OHSzhxisSk+kpKSQyWQDAwNRvBVQ6oGregUEBFhaWn7xVtwQhcvlnjp1SllZ2d7evqysTES5VFRUwNuu69ata25uFlEuUs9wCMgBUlJSMnPmTBwO980334h0klZVVZWbm5u8vPyBAwfEc2dNmhBnJC9YsEBGRqa1tVVSAjAMO3funLq6uqWl5bt373raR5JGAcOwhoaGxYsX43C4sLAwNFbWJ/Lz801NTU1NTZ89eyZpLSLhw4cPPj4+eDx+586dvJXLRASXy42OjiaRSDo6OufPnxd1dlKJ1AfkQKDRaD/99JOCgoKNjU1KSooYcmSz2YcOHVJQUHBxceFN+UT0BrFF8rZt2+Dci5UrV6ampopfQGlpqa+vr6ys7HfffSf4BoeEjQLk+vXrRCJRQ0Nj7969LS0tkpaDkDAVFRVhYWEEAsHKykq475UWTHNz8/r16/F4vJGR0W+//YZCETFwKisrt2zZoq6urqam9uuvv8Jp8GIjPz/f2dlZRkYmICAgPz9fnFkjBjOfPn0KDQ0lEAgWFhbPnz//4v6DwihgGEaj0Xbv3q2urk4kEo8cOYLa6OFJVVXVxo0bFRQUjI2Nz58/L+ZWFVJWVrZp0yYVFRUNDY1t27aVl5eLXwNCCnj16tVXX30lJyenp6d36NAhkT6JIAAul3vr1q2xY8fKyMgsW7YM2YVhTllZ2dq1a+Xk5ExMTM6dO9fLNnawGAVIY2Pjrl271NTUVFVVQ0NDhfJSNcSQIDU11d/fn0Ag6OjoHDx4UOJz5mk02rFjx/T19WVkZNzd3SMjI2k0mmQlIYYEVVVVx44dgxNjzczMjh07JvFgxjCMy+U+ePDAzs4OAODg4BAZGclgMCQtCiE+OBxOQkKCv78/Ho83NDQ8duxYR0dH7w8fXEYB0tTUdPToUbiEpLu7+6VLl7rM9UBIDbW1tb/++itcktnT0/PKlSt9Cl9R09nZee/evYULF8rLyysqKi5duvThw4eDod1HDDYaGxujoqKmTZsmKyurqakZFhYG3xIkaV3/A4fD+euvv+bPn4/H44lE4pYtW9AAg9Tz8ePHn3/+2dDQEIfDeXt7x8TE9GMO1mA0CjyysrKCgoIIBIKCgsLs2bOjoqJQr046aGhoiIqKmj17Nh6Ph6NHg3y+FZVKjYqK8vb2xuFwioqK3t7ex44dQ3clEB8+fDh27Ji3tzeBQJCVlfX29o6KihL1srMDp6am5uDBgyYmJgAAU1PT8PBw/sl0CCng48ePx44dc3d3x+FwGhoaoaGhAzGFOEy8a5v3g8bGxrt378bExDx58oRAIPj6+vr5+fn6+pJIJElLQ/SN9+/fx8XF3b59OzU1VVFRcfbs2f7+/r6+vr15G+wgoaqq6vHjxw8fPkxKSmIwGOPGjZs5c+bUqVNdXV3537GLkGIaGxvT0tISEhIePXpUVlZGJBJnzpw5a9YsHx8fdXV1SavrA1wuNz09/datW7dv366srDQzM1u4cKGvr6+bm9vgeVssovdwOJxXr179/ffft2/fzsnJ0dbWnj9//sKFC6dOnTrACh0CRoFHY2PjhQsXzpw5U1paCgCws7ObNm2aj4+Ph4eHGJakQPQPKpWalJQUHx+fkJDw8eNHdXX1mTNnLlq0aGj5g8/p6Oh4+vTpw4cP//rrr9LSUgKB4OTkNHHiRE9PTw8PD/6X3COkgJqampSUlNTU1GfPnuXn5+NwODs7u5kzZ86ePRs+ViBpgQMCw7CMjIxbt27dvXv348ePKioqkydPnjZt2rRp0ywtLSWtDvEFysrKEhISEhISkpKSmpqayGTy3LlzFy1aNHnyZFlZWaFkMTSMAoZhT548OXnyZGxsLJlM/uabb6ytrdPS0uLj4wsLC5WUlNzd3d3d3SdMmODi4oLaaIlTW1v7zz//pKenp6WlvXz5EgDg6Ojo4+Pj4+Pj6uqKx0tmPVDRUVlZ+ezZM3gheffunaysrJ2dnbOzs4ODg4ODw9ixY1H/bMhBp9Nfv36dnZ2dnZ2dmZlZXFyMx+Pt7e0nTpw4adIkDw8PTU1NSWsUCSUlJfCqk5ycTKVSDQwMJk2a5Obm5ubmZmtrK33/vEMRLpf77t27jIyM9PT01NTU9+/fKykpTZw4EXo7Gxsboec42I1Ce3v7xYsXT548WVBQMHHixA0bNsybN48/WMvLy+Pj458+fZqenv7x40dZWVlra2t3d3c3NzcXF5fRo0cPdbM/JGAymW/fvs3IyID+oLS0VEZGxtraesKECV5eXl5eXlpaWpLWKCbq6upSU1NTU1Ozs7PfvHlDp9Pl5eXt7OygabC3t7e0tBzSQynSCpVKffv27atXr6A5KCws5HA4WlpaDg4Ozs7Onp6eEyZMUFVVlbRM8cHhcF68eJGYmJiWlpaZmUmj0ZSVlR0dHSdMmODm5ubg4ADfG4kQDw0NDdnZ2bCNzcjIoNFoSkpKjo6OHh4eXl5e7u7u8vLyost98BqF1tbWiIgIuO7NsmXL1q9fD5/tEUBtbe3Lly+zs7OfP3+elpbW0dEhJyc3evRoh3+xt7eHK2EhBgiNRsvLy8vOzi4oKMjPz8/Ozu7o6FBRUbGzs/Pw8IADPMPHHPQEh8MpLCzM/hfoGwAAZDLZ2traysoK/h03bpyKioqkxQ4vqFTqhw8f8vPzYQAXFBR8/PgRwzB1dfWxY8fyWgwrKyscDidpsYOC0tLStLQ02Lq+fv2ay+VqaGhYW1s7ODjAMHZwcEAOWFhwOJxPnz7BphU2s/CGO5lMhg2sg4ODk5OTSM0BP4PRKLS0tERERBw+fJjJZK5atWrbtm1kMrmviXR2dubl5b158yYnJ+fNmze5ubktLS2ysrKjRo2ysrIy/5cxY8YQiURRnIXUgGFYeXl5cXFxcXFxUVFRUVFRYWFheXk5AIBEItnZ2Y0bN27cuHF2dnZjxoxB4zcC4HA4xcXF+fn57969g38LCwuZTKaMjIyJicno0aNN/2XUqFGmpqboJppQoFAopf/y4cOH0tLS4uLi2tpaAICqqqqlpaW1tTX8a2VlBR8EQAiGSqXCpjU3NzcnJ+ft27ednZ0EAsHS0tL8fxkxYoSkxQ4BaDRacXHx+/fvi4qK4IeCgoL29nYCgTBmzBg7OztbW1vYzErqajW4jAKFQvntt99Onz5NIBDCw8PDw8OFdSMQPi7y+vXrvLy8wsJCeNmD3TsNDQ0Y0yNHjjQyMjIyMjI2NjY2Nh6GEyRbWlo+8VFWVgYLqr29HQAwYsQIaK0sLCygM9DV1ZW05KENm82G/drCwsKSkhJ4MauqquJyuQAAbW1t6BsM/oVMJhsZGenq6qJbxV3o6Oioqqqqrq4uL9xp4zYAABwDSURBVC+vqamprKwsLy+H5dna2goAIBAIxsbGsDxHjx49duzYMWPGCP3ljcMTNptdVFQEHQNsMd6/f9/R0QEAGDFihJmZmZmZmYmJidG/GBsbD8+xh87OzoqKivLy8vLyctjGlpSUFBUVUSgUAICcnJypqamFhYW5ubm1tbWtra21tbWcnJykVQMweIwCg8E4ceLE/v375eXl165du2nTJjG8e7qyspLXUX7//n1ZWdmnT5/a2trgryQSydjY2MjISE9Pj0QikclkHR0dEomkq6uro6MzSOqvrzAYjLq6utraWgqFUltbW1tbW19fD6O2vLycSqXC3bS1tY2MjExMTHjjLqhzIDY6OzvLyspK+aisrKyqqqqrq4MGQkZGhkQiQd+gr69PJBJHjBihra2to6NDJBK1tbW1tbXFNiYpHhgMRkNDQ11dXQMftbW1dXV10Bk0NDTAPQkEAplMNjQ0NDAwMOXD0NBQWDPAEV+Ey+VWVFTwesklJSXwushrXYlEIjQNBgYGRCJRV1eXRCIRiUQymUwikYaujejs7IRNa11dXX19fU1NDYVCgf4VBircTVFREV5cRo0aZW5uDs2BsbHxoO0ASN4ocDicP//8c/fu3W1tbd9///3mzZsl+zx6U1MTvGrCLjWsXXhxhSMQkBEjRpBIJC0tLQ0NDQ0NDU1NTd5f+EFVVVVJSUleXl5NTQ2Px4vC93C5XBqNxmQy6XQ6g8Ho7Oxsbm6mUqnwL+8D/NvY2FhTU8P7RwUAaGhoQNNjYGAAoxaaA2NjYzSTYxDCYrFqa2srKipg17mioqK6urq6urqhoaGxsbGhoYHNZvN2VlVV1dHRgQEJ30gE/0J4GwkEgrq6uoyMjIaGBlyYRaS35FksVltbG//fjo6OlpYWGo3W0tLS3NzcwgeNRqNSqfDsGAwGLxF5eXlohqBrNzAw0NPTMzQ01NfX19PT09XVRbMKBi3Nzc3wkgmb1vLy8urq6vr6+urqajjwA1FRUSGTySNGjND4X/gbWDk5OVVVVXl5eSUlJWVlZVH03Nhsdmtra0dHR3t7O51OZzKZzc3NvNa1C01NTTU1NTQajXe4srIyNEC6urqwaYVtrKGhoY6OjtDVihQJG4XExMT//Oc/7969W7ly5c8//zzI11Ci0+nQKlIoFGgVm5qaPr8w81+M+SEQCCoqKoqKivCmBgxx3q8yMjL8q7VgGMbr30NaWlo4HA4AADay8B0z3WbU7f+Vpqamnp6ejo6Ojo4OHB2Rsk4noqmpqb6+ntfhrqurg9daeBnmXY9bWlq6hFYXupiGLoEKAFBQUOjS52ttbeW3KYAvXPltQU85ysrKqqmpaWpqqvGhrq6urq6uzQeRSCQSicPq0YPhQ0dHB69dhdbh80sy3NJTu6empgbXz+b/yvuV1/BCYP+K//Dm5mb4oaWlhc1mC/gf0egOTU1NaAt4A8/StAKbxIxCQUHB+vXrnz59unDhwv3795uZmUlEhihgs9nNzc2tra2wl88LO/6uPwAAWlTeUZ8HbpdAh+MT9+/fV1VVXbhwobq6Oh6PV1dX5/fU0BmgHhXii0DTwGKx4BWdRqNxuVzYCsO/vHazS6CCf61qQ0NDZmamj48PXGS9i3WA4QoAgKt0w7/QK8vJycFwVVZWVlRUVFNTQyNYiN4D7S+TyWxpaens7GQwGDBEaTQam82GffrPO1owaPPy8hgMhouLS5eOGeBrb2G4ampqwqCFsQ3jFhoCcZ7sIEECRqGjo2Pfvn2HDx+2s7P7/fffXV1dxSxgSPPzzz8fO3assrJSmuwqYiiSkJDg4+PT1NQ0PJtOxFBk9erVpaWlCQkJkhYyxBD3w2zPnj2zt7c/evTozz///M8//yCX0FfCwsIYDMaNGzckLQSBQCAQwwLxGYWmpqbVq1dPmTJl9OjRBQUFW7duRZOQ+4GOjs6iRYt+//13SQtBIBAIxLBATEbh9u3b5ubmf/311927d2NjY42MjMSTr1Syfv36N2/epKenS1oIAoFAIKQfkRsFOp0eEhKyaNGihQsX5ufnz507V9Q5Sj2urq5OTk4nT56UtBAEAoFASD+iNQpv3751dXW9devWtWvXIiMj0XNNwmLdunW3bt2qqqqStBAEAoFASDmiMgoYhh0/ftzR0ZFIJObl5QUGBoooo+FJYGCgpqbmuXPnJC0EgUAgEFKOSIxCU1PTjBkztmzZsmfPnsTERAMDA1HkMpyRl5f/5ptvIiMjuzzgjkAgEAiEcBG+USgsLHRxcSkqKkpPT9+6dSt6naCIWLNmTX19/Z07dyQtBIFAIBDSjJCv4vHx8W5ubtra2hkZGY6OjsJNHMGPoaHhnDlz0JRGBAKBQIgUYRqFs2fPzp49e8aMGU+ePEEvIBYD69evf/78eVZWlqSFIBAIBEJqEY5RYLPZGzZsCAsL27Fjx9WrV4fuS0KHFlOmTLG1tY2IiJC0EAQCgUBILUIwCgwGw8/P7+LFi3fv3t29ezd6I5E4WbNmzZUrVygUiqSFIBAIBEI6GahRoFKpPj4+WVlZycnJaDEl8bNixQplZeULFy5IWggCgUAgpJMBGYWGhoYpU6Z8+vQpJSUFTV2UCEpKSl999dXp06c5HI6ktSAQCARCCum/UWhqapo2bVpLS0taWpqlpaUQNSH6xLp16yorK2NjYyUtBIFAIBBSSD+NAo1GmzFjRlNTU2JiorGxsXA1IfrEqFGjpk+fjp6TRCAQCIQo6I9RYDAYvr6+tbW1T58+HTlypNA1IfrK+vXrk5KS3r59K2khCAQCgZA2+mwUOBzOsmXL3r9/Hx8fj1zCIMHX19fc3Bw9J4lAIBAIodNnoxAaGpqQkPDw4cMxY8aIQhCiH+BwuLCwsOjoaBqNJmktCAQCgZAq+mYUDh8+HB0dHRMT4+LiIiJBiP6xcuVKAEBUVJSkhSAQCARCquiDUYiLi9uxY8d///tfX19f0QlC9A8NDY3ly5efOHGCy+VKWgsCgUAgpIfeGoX3798HBAR89dVXmzZtEqkgRL/ZsGHDhw8fEhISJC0EgUAgENJDr4wCh8NZsWKFmZnZ6dOnRS0I0W+srKwmT56MnpNEIBAIhBDB92anQ4cOvX79OisrS15evstPra2tqqqqIhAmtfSvxLocRaFQEhMT6+vrvb29ra2tedvXr1+/aNGi4uJic3NzkeoZ5IjupHpZEcMZaYooVN3DBBS0gvnyiEJubu6ePXv27ds3duxY/u2RkZGTJk3ircn45MkTV1fXsrKy/ukYorDZ7NTU1B9++CEuLg5uEVAOXUqsl3x+VFFRUXBwsKenZ3Jysr29fUtLC++nuXPnGhsbnz17th8pS0cNnjp1ytPT09XVVegp96kihifSFFGouocJKGh7w5eNwrfffuvo6Lh58+Yu24ODg7lcLu8VA83NzRUVFXQ6XXBqNTU1/RM6OHn58uWFCxf2799fWVkJtwgohy4l1ks+P2rHjh1jx441NDSMioq6ePGimpoa7ydZWdnQ0NDz589/sSI+T1k6anD16tU0Gk0UMzr7VBHDE2mKKFTdwwQUtL3hC0bh+fPnSUlJ+/fvl5HpuqesrKyBgQHv68KFC6uqqgSPbFCp1KVLl/ZP6ODEzc1tw4YN/FsElEOXEuslnx8VFxenrq4OAFBXV/+8PENCQjo6Oq5cudLXlKWjBvF4vL6+vihS7mtFDEOkKaJQdQ8TUND2hi8YhZ07d06dOnXSpEn9zoAHnU4PCAgYokM6ApCTkxNndu3t7YLdrra29uLFi3///XcMw4SbtbTWYP/4YkUgvsgQiihU3QjI8AxaQZMZU1JSnj59mpqayr/x/v37jx490tTUbG9v5x9+qa+vv3HjhpOTE1yLKScn5+jRo1ZWVjU1NZ2dnadPn757925hYWFzc3NISIiFhcV3331XV1e3c+dOIyOj8vLyhoaGc+fOjRgx4s2bN1euXLl9+3ZeXt7GjRvv3btnamp6/fp1U1NTmNHjx48fPnxIIBBevHixatWqkJAQAACGYZGRkTk5Oa9evVJXVz916pSZmZngM+/2kBs3bgQHB2toaFRUVNBotN9//3337t3Ozs7//POPgNz56VIOAkpMMN0eFRUVlZiYCACIiYkpKSkZPXr01q1bPz9248aN48ePT01NnThxYi9T/lz54K/BbnPn/ZqUlHTo0KGsrCwnJ6eIiAiY++cnJdKKGCZITUT1dC6ouqUPFLR9A+uZZcuWubq68m+5cuWKq6tre3s7hmENDQ1EIlFXVxfDsLS0NE9PTwDArVu34J4WFhZpaWkYhjEYDE9PT7hx9uzZJiYmvNQmT568ePFi+NnOzm758uUYhtXU1Hh7ewMA1q1bl5+f//r1a3l5+cDAQLhbdHT0kiVLOBwOhmH79u0DACQlJWEYduDAgYsXL2IYxmazraysdHV16XS6gFMTcIiPj4+BgQFvNxsbG14hdJs7fBXTuXPnui2HnkpMMAKOamhoAAD88ssvglNwdXX19/fvfcpDsQa7zR3DsBkzZowYMWLVqlV///336dOnlZSU9PT02traejopAQy8IqSY+Ph4AMDZs2elJqJQdUs9oaGh3t7e0tQMiidoezQKNBpNSUkpMjKSt4VOp5PJ5KtXr/K2LFiwgKcJthqwfJlMJgDgxIkT8CfeIZ+X7/79++HnZcuW2draws/bt28HADQ0NMCvHh4eZmZmGIZRKBR1dfXS0lK4nUKhLFiwoKCgoKqqikQiwULHMOynn34CAFy/fl3AaQs4ZN68efxGwdXVFRqFnnLnNwpdykFwifWE4KN6WfeXL1/G4/EVFRW9T3lo1aCA3GfMmKGnp8fbbefOnQCA48eP93RSPSGUipBiYMCQSCTpiChU3cOB0NDQKVOmSE0zKLag7fHWw/Xr17lcbkBAAG9LampqTU2NjY0Nbwv/7XklJSXeZwKB4OPjs3HjxoKCgv379y9ZsqTbLJKTkwEA7e3tV65cefHiBfbvPXVZWVkAAB7/f9oMDAxKSkoAAGlpaVwul/fKSiKRePv2bVipLBZr9erVvJSDg4MVFRV7OjUAQHp6el8P6Sn3/Px8/t34y0FwifVE/47qgr+//3fffRcZGbl3795epjy0alBA7gAA/sm9K1eu/OWXX7Kzs3t5UjyEUhFST11dnXREFKruYQKVSpWaZlBsQdujUbh8+fL8+fM1NDR4WwoLCwEABAKhN+neuXMnJCQkIiLi9u3bMTEx3d4s53A4hw8ffv369fr1611cXDIyMgSn+fbtWxaLhWEYDofj3/7u3TtlZeU//vijN8L6fUhPuQugTyU2wKO6ICcnFxoaGhER8cMPPygoKPQj5UFeg73P3cTERE5Orr29vZcnxUMoFTEckI6IQtU9TIDz+1DQ9onun3qgUqn//PPPvHnz+DdCq/Lp06fepEsgEK5evXrp0iUAgI+PDzwffrhc7syZM/Py8m7evCm4veahpqbW0dFRUFDAv7Gzs1NJSamyspK3kgGkvr5eQFL9OKSn3AUc0qcSG+BRnxMWFkalUmNiYvqX8iCvwd7nLiMjg8fj4XJhXzwpfoRVEVKPdEQUqu5hAnzUHwVtn+jeKCQnJ3O53KlTp/JvtLW1BQDwX3h6Wj6os7MzIiICALB8+fKMjAwul/vkyRMAgIyMTFtbG9znxYsX8fHxXl5e8Ct0WIK1Ojo6AgB+/PFH3nI62dnZ165ds7GxwTCMf0rnhw8fBE9oF3AIHo9va2vjnVdbWxvMrqfcBeTS+xLr/VFfLCUeZDJ5wYIFx48f74eewV+Dvc+9rKyMxWIFBAT0dFI9IayKkHqkI6JQdQ8TVFRUAAravtLtzIU1a9Y4OTl9vn3SpEmysrIRERF0Ov3Fixd6enoAgCtXrtDpdHiX5cyZMxiGdXR02NjYsNlsDMOYTKa2tnZ6ejqGYWFhYQCArKysp0+fwhL39PTMzc29cOGCjY2NiopKTk5ObW0tXMKINwdkypQp6urqXC4XwzD4huspU6acPHlyy5Ytfn5+LBaLy+U6OTkBABYsWHDp0qVTp055eXnV19cLmJoh4JA9e/YAAPbu3VtUVLR3714zMzMNDY3s7Oyecn/+/DkA4Pjx4zBl/nIQXGIC5Ak4Kjs7GwCwY8cOAYfzgI+2ZmZmSl8NwvG9bnOfNWsWiUSCjzlwudxVq1bB6Tw9nZQYKkIqgdO+3N3dpSOiMFTdwwD41IPUNIOYuIK2e6NgYWHRbepUKnXlypUkEsnIyGj37t2hoaErV65MTExMSkqaPHkyAMDZ2TkhIaGjo8PJyWnWrFmHDx8ODQ09e/YsPDwnJ8fAwMDc3DwmJgbDsLCwMFVVVVdX18TExEePHmlray9atCg2NhbO8li7di2FQrl8+bKysjIAYPfu3Ww2m06nr1mzRl9fn0QirVmzhkqlwpQbGxuXLVumo6NDJBJXrFhRVVX1xTPv6RAajebn56eiouLq6vry5ctVq1Z9/fXXjx49wjDs89wzMzNnzJgBAHBwcHj8+HFycjJ/OQgoMd7U1m7p6aisrCy4utbIkSOvXLnCO30BODg4BAUFSWUNdpt7W1tbTk5OYGDgjBkzQkNDN27cyHvSqaeTEk9FSB/QKJSVlUlNRKHqlnqgUZCmZlA8QYvDPhudaGlp0dDQuH//vp+fX1/HJxCDjXPnzq1bt668vJxEIklaC0KqSEhI8PHxaWpq0tTUlLQWBKJXrF69urS09P+1d3chUXQPGMBnZmdKzbXSTAO/yjRTrDVTsiSTNCvLqChtl13tCwNFoQ+TjL72Iu+EsoiMCvGuwLqIyoVAQkIiDC3DCxPRQvvQ/MrYmd15L+Zl33V31e2PO2fH//O7iHYNeVjPzj6e5pxjMplIB1EYF6se3r9/L4piUlKS/GnmVlhY2HQ3G9bX10uTP6TIlk2n01VWVt67d6+qqmquvqdsZHiVvHmQwJzDjxsUxxsGrYui0NbWFhQU9D8cX+RtHG4f9SqyZfP19T127Njt27crKioUt/RLhlfJmwcJzDn8uEFxvGHQulj10N7ertFo5I8CHlJSUjI4OPj06VPSQQAAQHlcFIXPnz+vXr1a/ijgIZGRkbm5ubW1taSDAACA8rgoCr29vZGRkfJHAc8pLS1tbm5ub28nHQQAABTGsShYLJb+/n4UhXkmKysrLi7u1q1bpIMAAIDCOBaFgYEBnucjIiKIpAEPoWm6pKSkoaFhaGiIdBYAAFASx6IgHUwZHBxMIgx4UGFhIcuyDx48IB0EAACUxLEo/Pr1i6Io+0MjYX5Qq9UGg6G2tnbWwyYAAABsHIvC8PAwhaIwT5WVlfX29j5//px0EAAAUAzHojA6Ourj47Nw4UIiacCjYmJisrKysE4SAADc51gUzGazdMQ1zEulpaVNTU1dXV2kgwAAgDI4FgVBEFjWxb7OMD/s2bNn5cqV0iHr9mwnowMAANhz7AQ8z9M03dnZ+ePHj58/f3779m3JkiX5+flEwsGcYxjm1KlTRqPx2rVrAQEBExMTDQ0NN2/ebGxsjImJIZ0OvNrIyMj169dtDwcHB6OiooxGo20O0t/f/+LFi4TSAbjw+vXrZ8+e2R52d3ePj49XVlbanklLS9u3bx+JaEpCf/r0qaKiYmBg4Pv378PDwyMjIw7/4vz589XV1UTCgScMDw+Hh4efO3duZGSkrq7u9+/fVqu1tbU1NTWVdDTwdrGxsd3d3S5PFzObzVqttqGhQf5UANPp6OhYt24dy7IqlcrhS1arlef5J0+eoCjMio2JiWlra5vhfKqsrCw5A4FHiaL47t276Ojoq1evsizL87z0vLQsFmBmer3eaDROd+itTqeTOQ/AzBITE9esWdPV1SUIgvNX1Wr1zp075U+lOIxKpTp9+rRz25JwHLdlyxaZM4EnjI6O3r17NzY2Njs7u7OzUxRFW0ugaRpFAdyh1WpdXnApilq8eDF+qQAvZDAYXM6BcRyXn5+PJX7uYCiKOnHihI+Pj/PXaJpOS0vz9fWVPRXMscnJyZSUlOLi4u7uboqiHK71LMtK+2cAzCw6Onr9+vU0TTs8z3GcTqdzeTkGIEun07lstzzPa7Va+fMoEUNRlFqtPnnypPObnOO4nJwcEqlgjvn6+j5+/Njf359hXJwXyjAMZhTATQaDwXkCkuf5I0eOEMkDMLPIyMjk5GTndrts2bKtW7cSiaQ4/35slJeXO+/sazabMZc4byQmJppMJpZlXXYFFAVwU0FBgfNi2tDQ0M2bNxPJAzAr53bLcZzLygsu/fuZERUVlZeX5zCp4O/vn5ycTCIVeMSmTZsePXrk/LzVakVRADetWLEiPT3dvm4uWLDAYDC4LKAA3qCgoEAURftnMAf2V/57b585c8Z2dxtFUQzDZGZmonDNM3v37n348KHDLJwgCLhHAdyn1+vth5DZbMY1F7xZcHBwRkaG/cdZeHg4fg12339FIT09PSkpyfZSMgyzY8cOQqnAg6QVbvYXelEUpePFAdxx8OBB+/mDVatWaTQagnkAZqXX622TChzHHT161PmuBZjOlNnCs2fP2v73URAE3KAwX1VVVZWVldlf64eGhgjmAWVZunRpTk6OtNc7x3FFRUWkEwHM4sCBA7bTCXiex3bDf2VKUTh06FBISIj09+Dg4Li4OBKRQA41NTVardb2zkFRgL+i0+mk2595ni8oKCAdB2AWAQEBubm5LMvSNJ2YmBgfH086kZJMKQocx5WXl0u7XWJh5PxG0/T9+/czMjKkrjA6Oko6EShJXl6etPmKRqPBKSGgCFK7ValUer2edBaFcbxRubi4mGVZi8WSnZ1NJBDIhuO4xsbGhIQEiqLGxsZIxwEl8fPz279/P0VRhYWFpLMAuCU3N9fPz89isRw+fJh0FoVxLAp+fn5JSUkURb18+bKjo4NEJJCPWq1uamoKCwsTBGHXrl1XrlxxPhUMwFlvb+/Y2BhN0319fRgzoAjNzc3Lly8PDAxsaWlxWC0JM6PtXy9RFHfv3m0ymSwWi7SnQmtrq9QbYL7q7+9fu3btxMSEKIocx8XFxb19+xb7n8MM+vv7ExIS/vz5YzabMWZAEerr64uKimiaFkWRpunLly9funSJdCjFmDKj8PHjxxcvXtjuURJFsaamhlAwkEldXZ3ZbJb6Is/zHz58MJlMpEOBV6urq5NaAoUxAwphNBpFUbRardKf1dXVzpsRw3SmFAWHw6YFQejp6ZE3D8jty5cv9jvysizb19dHMA94P4wZUJyvX7/aP5ycnMR2tO6bUhQ0Go1tvRxFURzHpaenyx4JZJWSkmL/UBCE1NRUUmFAETBmQHE2btxo+3RjGCYiIiIoKIhsJAWZUhRCQ0Nv3LihUqmkrXji4+MvXLhAKBjI5Pjx49u3b6coimEYmqYrKyuxsynMDGMGFOfOnTuBgYE0TdM0vWjRovr6etKJlIR2vvmzp6fnzZs3ISEh27Ztw1kP/ydaWlp6eno2bNiAfUjATRgzoCzj4+OvXr0ym82ZmZmYTvgrLooCAAAAgAQnwwIAAMC0UBQAAABgWigKAAAAMK1/APE2tbm5BEG1AAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1148,12 +1144,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1167,35 +1163,35 @@
      "text": [
       "HEAD points_ddf:\n",
       "          x         y\n",
-      "0  0.887968  0.582714\n",
-      "1  0.146722  0.296758\n",
-      "2  0.391815  0.623228\n",
-      "3  0.882974  0.621067\n",
-      "4  0.794594  0.844349\n",
+      "0  0.994778  0.920240\n",
+      "1  0.536145  0.522197\n",
+      "2  0.552025  0.939834\n",
+      "3  0.597529  0.873719\n",
+      "4  0.374750  0.841134\n",
       "\n",
       "HEAD df_w_cudf:\n",
       "          x         y  distance_cudf\n",
-      "0  0.887968  0.582714       1.062094\n",
-      "1  0.146722  0.296758       0.331048\n",
-      "2  0.391815  0.623228       0.736161\n",
-      "3  0.882974  0.621067       1.079522\n",
-      "4  0.794594  0.844349       1.159442\n",
+      "0  0.994778  0.920240       1.355147\n",
+      "1  0.536145  0.522197       0.748426\n",
+      "2  0.552025  0.939834       1.089963\n",
+      "3  0.597529  0.873719       1.058502\n",
+      "4  0.374750  0.841134       0.920839\n",
       "\n",
       "HEAD df_w_numba:\n",
       "          x         y  distance_numba\n",
-      "0  0.887968  0.582714        1.062094\n",
-      "1  0.146722  0.296758        0.331048\n",
-      "2  0.391815  0.623228        0.736161\n",
-      "3  0.882974  0.621067        1.079522\n",
-      "4  0.794594  0.844349        1.159442\n",
+      "0  0.994778  0.920240        1.355147\n",
+      "1  0.536145  0.522197        0.748426\n",
+      "2  0.552025  0.939834        1.089963\n",
+      "3  0.597529  0.873719        1.058502\n",
+      "4  0.374750  0.841134        0.920839\n",
       "\n",
       "HEAD df_w_cupy:\n",
       "          x         y  distance_cupy\n",
-      "0  0.887968  0.582714       1.062094\n",
-      "1  0.146722  0.296758       0.331048\n",
-      "2  0.391815  0.623228       0.736161\n",
-      "3  0.882974  0.621067       1.079522\n",
-      "4  0.794594  0.844349       1.159442\n",
+      "0  0.994778  0.920240       1.355147\n",
+      "1  0.536145  0.522197       0.748426\n",
+      "2  0.552025  0.939834       1.089963\n",
+      "3  0.597529  0.873719       1.058502\n",
+      "4  0.374750  0.841134       0.920839\n",
       "\n",
       "Max Difference cudf to numba: 2.220446049250313e-16\n",
       "Max Difference cudf to cupy: 2.220446049250313e-16\n"
@@ -1267,12 +1263,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Hyx2JiOS1mjOYRCQpHx8fpKWlwd3dHaGhoeb7NVLblJGRgb59++Lo0aP4/vvvWS6JCABPkRNRM+jatStSUlIwb948zJw5E88//zzOnz8vdyySkMFgwPz58xEeHg5/f3+cOnXKfI9RIiIWTCJqFgqFAh988AFSU1Nx6dIlBAUFYd68eaioqJA7GjWBEAJbtmyBv78/1q1bh7/97W/49ttv4e7uLnc0ImpFWDCJqFmFh4fjp59+wooVK/DFF1/Az88PcXFxuH79utzRqBFMJhN2796NkJAQvPHGGxg9ejRyc3MxY8YM8w3uiYhuYcEkomanVCoxY8YM5Obm4rXXXsPSpUvh5eWFRYsWQafTyR2P7qO2thabN29GUFAQXnrpJXTv3h1ZWVlYu3Yt73dKRPfEq8iJqMVVVFTgyy+/xCeffIJr165h9OjRmDZtGoYNG8bZsFbi/Pnz2Lp1KzZu3IiioiK88sor+K//+i8EBATIHY2IWr/VLJhEJBuj0Yht27Zhw4YNSE9Ph6+vL6ZMmYIJEybA19dX7niPnfLycuzduxdffvkljhw5gq5du2LKlCl488030b17d7njEdGjgwWTiFqHnJwcfPHFF0hISEBpaSn69OmDsWPHYuzYsQgMDJQ7Xpul0+mwZ88e7Nq1CykpKQCAUaNG4c0338Tw4cOhUChkTkhEjyAWTCJqXerq6nD48GHs2rULe/bsQVFREfz8/DBixAgMGzYMQ4YMgbOzs9wxH1l1dXXIyMhAcnIyDh06hBMnTsDa2hrDhw/H2LFjMWrUKHTs2FHumET0aGPBJKLWy2Qy4cSJE9i3bx+SkpJw6tQpWFlZoX///oiIiEBYWBhCQkLg4uIid9RWq7q6GidPnkR6ejqSk5Pxww8/oKKiAt26dUNERASee+45jBw5Eu3bt5c7KhG1HSyYRPToKC0tRWpqKpKSkpCSkoILFy4AAHr06IGQkBCEhISgf//+CAoKgoODg8xpW15dXR1yc3Px008/ISMjA5mZmTh16hRqamrQqVMnDB48GMOGDUNERAR69Oghd1wiartYMIno0aXT6cxF6tb/6vV6WFlZwdvbG4GBgQgKCkJQUBB69uwJX19fdOjQQe7YTXbjxg3k5eUhNzcXOTk5yM7ORk5ODs6ePYuamhpYW1ujT58+CA0NNRdvFkoiakEsmETUdgghcPHiRWRnZ+PMmTPm4nXu3DnU1NQAADp06AAfHx/zy8vLC127doW7uzvc3d3h4eEh6+xndXU1dDodrly5Aq1Wi+LiYhQUFOC3334zv4qKinDrr+7bi3RgYCACAgJgY2Mj2xiI6LHHgklEbV9tbS0uXrxoUdJuvQoKCu642bu9vT06d+4MFxcXdOzYEQ4ODnB0dDS/bl0E4+joCKVSCQCwsrKymB2trKw0l1oA0Ov1EELAYDCgoqLC/NLr9aioqEB5eTmKi4tRVlZmkaV9+/bw8vKyKMXe3t7w8fHBk08+CUdHx+Y6bERED4sFk4iotrYWOp0OxcXFKC4uNs8c6vV6lJWVWRTCW6UQ+FdpBID6+nqLx1/a2trCzs7O/P5WGW3fvr25qDo5OaFDhw7mP3fu3BldunSBm5sb1Go11Go17O3tW/ZgEBE1HQsmEZFU6uvroVQqkZiYiOjoaLnjEBHJZTWfRU5EREREkmLBJCIiIiJJsWASERERkaRYMImIiIhIUiyYRERERCQpFkwiIiIikhQLJhERERFJigWTiIiIiCTFgklEREREkmLBJCIiIiJJsWASERERkaRYMImIiIhIUiyYRERERCQpFkwiIiIikhQLJhERERFJigWTiIiIiCTFgklEREREkmLBJCIiIiJJsWASERERkaRYMImIiIhIUiyYRERERCQpFkwiIiIikhQLJhERERFJSil3ACIierRVVFTA0dHRYplWq0VSUhJ0Oh0iIiIQGBgoUzoikgNnMImIWrGUlBRoNBrk5eXJHeUO69atw+DBg9GzZ0+L5efOncPUqVMRHh6O1NRU9O3bF9evX5cpJRHJgQWTiKgVKysrw6VLl1BZWdnozxYVFTVDon+ZOnUqTCYT6uvrLZYvWLAAQUFB6NatGzZv3oxNmzbBycmpWbPcrrnHTkT3x4JJRNSKRUdHo7CwsNGnmPV6PSZOnNhMqW5SKBTw9PS8Y/nBgwfh7OwMAHB2dm72HLdribET0f2xYBIRtTGVlZUYN26cLKfVq6qqHmq2VSpyjp2I/oUFk4hIYpcvX8bChQsREBCAwsJCjBkzBi4uLggJCUF6errFuomJiZg5cybmzp2LkSNHYtGiRaiurjb/XKfTYdWqVcjIyAAAnDp1Cu+99x58fX1RWVmJqVOnwtXVFSEhIbh48SIAYPfu3Th79ixKS0sRGxuL5cuXAwB+/vlnvP7664iLi8O7776L6dOnN3pse/fuxbRp0zBv3jzMmjXL4lT05s2bMW3aNADAjh07EBsbi08++aRR27/f8di+fTscHR3RrVs3AEB5eTmWLFkChUKBAQMG3HfsRNTCBBERSaKurk4AEEuWLBEBAQFCoVCIOXPmiNTUVLFz507RqVMnYW9vL65cuSKEEGLFihUiLCxM1NTUCCGEKC0tFX5+fmLQoEHCZDKJtLQ0ER4eLgCIxMREIYQQRUVFIiIiQgAQM2bMEDk5OeLkyZPCxsZGvPLKK+Yso0aNEt7e3hb5/P39RVpamhBCCKPRKMLDwxs1vq1btwqNRiOqqqrMed3c3IRarTavU1paKgCIjz76qJFH78HHQwghIiMjhaenp8XnevXqJTQajfn93cZORC1qFWcwiYgk1rNnT4SGhsLKygpxcXEYMmQIxo4dizVr1sBoNCI+Ph5arRaLFy/G22+/DZVKBQDo1KkTFixYgCNHjiAhIQFhYWFYvHixxbbVajX69+8PAPjwww8REBCAPn36oH///sjKyrpnptraWpw7dw4nT54EANjZ2eGdd95p8JiMRiPmzp2LWbNmwdbW1pw3PDy8UcfmXhpyPADA3t7+js+2b99ekgxEJB0WTCKiZqBQKKBUKs1lCQCioqJgbW2N7OxspKeno7Ky0ny695ZRo0YBAA4fPgzg7oVKoVAAAJTKf93K2NPTExUVFffMo1KpEBkZidmzZ2P69OnQ6/WYMGFCg8dz9OhRFBUVoVevXhbLra2tG7yN+2no8SCiRwMLJhFRC1GpVPDw8EBdXR3y8/MBANeuXbNYx9XVFfb29rhy5Yrk+9+1axfGjx+PtWvXwt/fH0eOHGnwZ8+ePQsAFoVZSnIcDyJqPiyYREQtqKamBv7+/vDx8QEA84U5t/P395d83yqVCtu2bcOWLVsAAJGRkebi+CC3ZipvFUGpyXE8iKj5sGASEbUQrVaL4uJiREdHQ6PRwNHREXv27LFYp7CwEEajEaNHj27Svtq1aweDwWB+X11djbVr1wIAYmJikJ6eDpPJhJSUlAZtLzg4GMDNq8N/7/YbrQshHipvQ4+HUqmEwWCw2KfBYIDJZDK/v33sRNTyWDCJiJpJdXU1srOzze+XLl2KmJgYaDQauLq6YtmyZTh27BiSk5PN63z66aeYPHkyhg4dCgAoKSkBAJSWlprXKS8vBwDU1dWZl5WUlKCqqspc8Dw8PFBaWoqsrCz88MMPMBqN2LBhg7mYeXp6wtnZGX379m3QWMLCwjB48GBs3LgR8fHxMBqN+PHHH5GWlgadTodt27bBaDSioKAAwM2LghqjocejV69e0Ov1WLZsGXJzc/HRRx+huroaubm5+Omnn+45diJqWYoPPvjgA7lDEBG1BUII/PnPf8a4ceNw4cIFZGdnw2g0IiEhAYcOHYJarcaKFStgZWUFAAgJCUHv3r2xcuVKZGZmIiMjAy4uLoiLi4OVlRUOHz6MuLg45OXlQavVwsfHB/n5+Vi+fDn0ej0MBgNCQkKwZ88erFu3DhUVFbCyskJ4eDi8vLxw4MAB7Nu3DxqNBkFBQdiyZQv279+PwsJCJCQkYPLkyRgzZkyDxxcVFYXi4mKsX78e8fHxcHBwQJcuXRAcHIwBAwbAYDBgxYoVyM7OxuXLl+Hm5obu3bubrzp/kAcdDwDo27cvcnJykJCQgOPHj2PWrFnQarXw9/eHl5cX/Pz80L17d4ux9+7du/G/TCJqih+txMOezyAiIgv19fVQKpVITEzE999/j4SEBFRVVckdi4iopa1WPngdIiJqyzw9PS2eHnQ3X331FUaOHNkqt09ErQ8LJhFRM7h27RpqampgMBjg4OAgd5z7unz58iO9fSJqfXiRDxGRxLZu3YqDBw/CZDJh1qxZSEtLkzsSEVGL4gwmEZHEJk2ahF27dskdg4hINpzBJCIiIiJJsWASERERkaRYMImIiIhIUiyYRERERCQpFkwiIiIikhQLJhERERFJigWTiIiIiCTFgklEREREkmLBJCIiIiJJ8Uk+REQPKT8/H/X19eb3t/5cUlKCixcvWqzr4eEBW1vbFs1HRCQXKyGEkDsEEdGjaMSIETh48OAD17O2tkZxcTE6duzYAqmIiGS3mqfIiYge0oQJE2BlZXXfdRQKBYYPH85ySUSPFRZMIqKHFBUVBZVKdd91TCYTYmJiWigREVHrwIJJRPSQnJyc8Pzzz0OpvPfX2W1tbTFq1KgWTEVEJD8WTCKiJpg0aZLFhT6/p1KpEB0dDXt7+xZORUQkLxZMIqImeP755+9ZIGtrazFx4sQWTkREJD8WTCKiJrC1tcVLL70Ea2vrO37m7OyMiIgIGVIREcmLBZOIqIkmTpyImpoai2UqlQqTJk164EVARERtEQsmEVETDRs2DC4uLhbLamtrMWHCBJkSERHJiwWTiKiJFAoFJk2aZHGaXK1WIywsTMZURETyYcEkIpLAhAkTzKfJVSoVXnvttQfehJ2IqK1iwSQikoBGo4GnpyeAm6fHX3nlFZkTERHJhwWTiEgCVlZWmDx5MgDA19cXffr0kTkREZF8WDCJiCRy65ZEI0aMkDkJEZG8WDCJiCSwatUqDB8+HACwZs0ajBo1CnV1dTKnIiKSBwsmEVET/fOf/8Ts2bMtCuXBgwexZs0aGVMREcmHBZOIqIkyMzPvWGYymZCWliZDGiIi+bFgEhE1UefOnWEymSyWKRQKqNVqmRIREcmLBZOIqIkGDhyI3r17mx8LqVQq0a5dO7z99tsyJyMikgcLJhFRE1lbWyMlJQVz5sxB165dERkZiYyMDAQEBMgdjYhIFlZCCCF3CCKitqC+vh5KpRKJiYmIjo6WOw4RkVxWcwaTiIiIiCTFgklEREREkmLBJCIiIiJJsWASERERkaRYMImIiIhIUiyYRERERCQpFkwiIiIikhQLJhERERFJigWTiIiIiCTFgklEREREkmLBJCIiIiJJsWASERERkaRYMImIiIhIUiyYRERERCQpFkwiIiIikhQLJhERERFJigWTiIiIiCTFgklEREREkmLBJCIiIiJJsWASERERkaRYMImIiIhIUiyYRERERCQpFkwiIiIikpRS7gBERPRoq6iogKOjo8UyrVaLpKQk6HQ6REREIDAwUKZ0RCQHzmASEbViKSkp0Gg0yMvLkzvKHdatW4fBgwejZ8+eFsvPnTuHqVOnIjw8HKmpqejbty+uX7/+wO215rESUeOwYBIRtWJlZWW4dOkSKisrG/3ZoqKiZkj0L1OnToXJZEJ9fb3F8gULFiAoKAjdunXD5s2bsWnTJjg5OT1we00ZKxG1LiyYREStWHR0NAoLCxt9ilmv12PixInNlOomhUIBT0/PO5YfPHgQzs7OAABnZ+cG53jYsRJR68OCSUTUxlRWVmLcuHGynGquqqriDCQRsWASEUnt8uXLWLhwIQICAlBYWIgxY8bAxcUFISEhSE9Pt1g3MTERM2fOxNy5czFy5EgsWrQI1dXV5p/rdDqsWrUKGRkZAIBTp07hvffeg6+vLyorKzF16lS4uroiJCQEFy9eBADs3r0bZ8+eRWlpKWJjY737AgAAACAASURBVLF8+XIAwM8//4zXX38dcXFxePfddzF9+vRGj23v3r2YNm0a5s2bh1mzZlmcht+8eTOmTZsGANixYwdiY2PxySefNHjbDzNWImqlBBERSaKurk4AEEuWLBEBAQFCoVCIOXPmiNTUVLFz507RqVMnYW9vL65cuSKEEGLFihUiLCxM1NTUCCGEKC0tFX5+fmLQoEHCZDKJtLQ0ER4eLgCIxMREIYQQRUVFIiIiQgAQM2bMEDk5OeLkyZPCxsZGvPLKK+Yso0aNEt7e3hb5/P39RVpamhBCCKPRKMLDwxs1vq1btwqNRiOqqqrMed3c3IRarTavU1paKgCIjz76qFHbbspYiajVWcUZTCIiifXs2ROhoaGwsrJCXFwchgwZgrFjx2LNmjUwGo2Ij4+HVqvF4sWL8fbbb0OlUgEAOnXqhAULFuDIkSNISEhAWFgYFi9ebLFttVqN/v37AwA+/PBDBAQEoE+fPujfvz+ysrLumam2thbnzp3DyZMnAQB2dnZ45513Gjwmo9GIuXPnYtasWbC1tTXnDQ8Pb9SxuRcpx0pE8mPBJCJqBgqFAkql0lweASAqKgrW1tbIzs5Geno6Kisr0a1bN4vPjRo1CgBw+PBhAIC9vf1dtw0ASuW/bmXs6emJioqKe+ZRqVSIjIzE7NmzMX36dOj1ekyYMKHB4zl69CiKiorQq1cvi+XW1tYN3saDSDVWIpIfCyYRUQtRqVTw8PBAXV0d8vPzAQDXrl2zWMfV1RX29va4cuWK5PvftWsXxo8fj7Vr18Lf3x9Hjhxp8GfPnj0LABaFmYjoXlgwiYhaUE1NDfz9/eHj4wMA97xYxd/fX/J9q1QqbNu2DVu2bAEAREZGmovjg9yaqbxVjImI7ocFk4iohWi1WhQXFyM6OhoajQaOjo7Ys2ePxTqFhYUwGo0YPXp0k/bVrl07GAwG8/vq6mqsXbsWABATE4P09HSYTCakpKQ0aHvBwcEAbl4d/nu332hdCNGk3ETUNrBgEhE1k+rqamRnZ5vfL126FDExMdBoNHB1dcWyZctw7NgxJCcnm9f59NNPMXnyZAwdOhQAUFJSAgAoLS01r1NeXg4AqKurMy8rKSlBVVWVueB5eHigtLQUWVlZ+OGHH2A0GrFhwwZzGfT09ISzszP69u3boLGEhYVh8ODB2LhxI+Lj42E0GvHjjz8iLS0NOp0O27Ztg9FoREFBAYCbFwU11sOOlYhaH8UHH3zwgdwhiIjaAiEE/vznP2PcuHG4cOECsrOzYTQakZCQgEOHDkGtVmPFihWwsrICAISEhKB3795YuXIlMjMzkZGRARcXF8TFxcHKygqHDx9GXFwc8vLyoNVq4ePjg/z8fCxfvhx6vR4GgwEhISHYs2cP1q1bh4qKClhZWSE8PBxeXl44cOAA9u3bB41Gg6CgIGzZsgX79+9HYWEhEhISMHnyZIwZM6bB44uKikJxcTHWr1+P+Ph4ODg4oEuXLggODsaAAQNgMBiwYsUKZGdn4/Lly3Bzc0P37t3NV53fT1PG2q4d50qIWpkfrQT/CUhEJIn6+noolUokJibi+++/R0JCAqqqquSORUTU0lYrH7wOERG1ZZ6enhZPD7qbr776CiNHjmyV2yei1ocFk4ioGVy7dg01NTUwGAxwcHCQO859Xb58+ZHePhG1PvziChGRxLZu3YqDBw/CZDJh1qxZSEtLkzsSEVGL4gwmEZHEJk2ahF27dskdg4hINpzBJCIiIiJJsWASERERkaRYMImIiIhIUiyYRERERCQpFkwiIiIikhQLJhERERFJigWTiIiIiCTFgklEREREkmLBJCIiIiJJsWASERERkaSshBBC7hBERI+iiIgIZGZm4vd/jdbW1kKpVMLKysq8TKVS4fTp0/Dw8JAjJhFRS1vNZ5ETET2kESNGIDk5+Y7l1dXV5j9bWVmhf//+LJdE9FjhKXIiooc0ceJEtGt3/79G27Vrh9dee62FEhERtQ4smERED8nDwwMDBgx4YMl86aWXWigREVHrwIJJRNQEkydPtvi+5e+1a9cOQ4cOhbu7ewunIiKSFwsmEVETjBs37p4FE7hZQImIHjcsmERETdCxY0c8++yzUCrvvGZSqVRizJgxMqQiIpIXCyYRURPFxMTAZDJZLFMqlRg9ejScnJxkSkVEJB8WTCKiJhozZgysra0tltXX12PSpEkyJSIikhcLJhFRE7Vv3x6jR4+GSqUyL7Ozs8OIESNkTEVEJB8WTCIiCUyaNAm1tbUAbj6555VXXoGtra3MqYiI5MGCSUQkgZEjR5q/b1lbW4uJEyfKnIiISD4smEREElCpVBg3bhwAoFOnThgyZIi8gYiIZMRnkRPRY81oNKK6uhplZWWoq6tDRUUFqqurYTQaUVlZiZqaGpSXl8NkMqG+vh7Xr1+/Yxu/Xx8AvL29sXDhQqhUKjg4ONyxvp2dnfn0uZOTE5RKJTp06GBe39bWFnZ2dnBwcICNjQ2cnZ2b9yAQEUmMBZOI2gSj0Yji4mKUlJRAq9WipKQEpaWl0Ov193yVl5ejpqbmgdt2dHQ03+fS2dn5jkdDKhQK8+lxlUqFyspKJCUloaqqCjdu3Lhje9evX0d9fT0AQK/XQwjxwAwdO3ZEhw4dzC9nZ2eL9y4uLujcuTPUajXc3NzQuXNnuLi4PHC7RETNwUo05G82IiKZ1NXVoaioCAUFBSgoKMClS5fML51Oh5KSEhQXF5tnD29xdnaGu7u7RQm728vZ2Rm2trbo0KEDlEolHB0dYWNjA3t7e9jb28PGxqZReVevXo3p06ff9+k+tzOZTCgvL0dtbS0MBgNu3LiBqqoqVFZWorq6Gnq9HmVlZXctyLf+fPXqVeh0Oov7cVpbW5vLplqtRufOneHl5YXu3bujW7du6NatG7p37w47O7tGjZGI6AFWs2ASkez0ej3Onz+P3Nxc5Obm4sKFC8jPz0dBQQGKiopQV1cH4ObNy7t27WouR+7u7nB3d0eXLl0sipS7u3uji6FUhBCNKpdSMplM0Gq10Ol05tlcnU6HoqIiaLVaFBUV4dKlS8jLy0NVVZX5c+7u7uZj+sQTT6BHjx7w8/NDjx490LVrV1nGQkSPNBZMImo5BQUFyM7OxunTp81lMjc3F1qtFsDNGTdfX1/06NED3t7e5tLTrVs3eHl5Qa1WQ6FQyDyKtqG0tNQ8E5yfn2/+84ULF5Cbm2v+rqmDgwP8/PzMhTMgIADBwcHw9/e/6+MxiYjAgklEzcFoNCInJwc///wzfvnlF/OrrKwMAODl5YUePXpYvPz8/ODt7c0C2UqUlJTg3Llz5pnl388w19bWwsbGBoGBgQgODkavXr3Qu3dvBAcHw83NTe7oRCQ/FkwiarorV67g2LFjSEtLQ1ZWFv7xj3+guroa1tbWePLJJ9GvXz/069cPgYGB6N27N0vII6yurg7nzp3DmTNnkJOTg6ysLJw5cwYXL14EAHTp0gX9+vXDwIEDERYWhv79+8v2dQUikg0LJhE1Tn19PX766SckJyfj+PHjSE9Ph06ng7W1NZ566iloNBpoNBr069cPvr6+d1xxTW2TVqvFyZMnkZGRgfT0dKSnp6OsrAx2dnbo168fNBoNnnnmGQwePBjt27eXOy4RNS8WTCJ6sPPnzyMpKQnJyclITU3FtWvXoFarER4ejgEDBiA0NBT9+vXjTBWZCSFw7tw5pKen48SJEzh+/DhycnKgUqmg0WgQERGBiIgI9O/fn9/lJGp7WDCJ6E719fU4evQodu3ahX379iE/Px8ODg4YPHiwuRgEBQXJHZMeMcXFxUhOTkZSUhKSkpJw+fJlODk5ITIyEtHR0Xj++efh6Ogod0wiajoWTCK6qba2Fqmpqdi5cyf27NkDrVaLoKAgvPjiixg+fDhCQ0OhUqnkjkltyNmzZ5GUlIS9e/fi8OHDUCqVePbZZxEdHY3Ro0ejY8eOckckoofDgkn0uPv111+xfv16fPXVV7h69SqeeuopREdHIzo6Gv7+/nLHo8fE1atXsW/fPuzcuRNJSUkwmUx44YUXMG3aNDz77LP8Li/Ro4UFk+hxdOPGDSQmJmL9+vU4cuQIvL298eabb2LixInw9fWVOx495srLy7F371588cUXOHr0KLy9vTF16lRMmTIFXbp0kTseET0YCybR46SyshKrVq3C8uXLUV5ezhkiavV+P8N+/fp1xMTEYNGiRfyHEFHrxoJJ9DgwGo1Ys2YN4uLicOPGDcycORMzZ86EWq2WOxpRg9y4cQNff/01li5divz8fLz66qtYtGgRvL295Y5GRHdiwSRq67Zv3445c+bAYDBgxowZmDt3LlxdXeWORfRQ6urqkJCQgI8++ggFBQWYM2cOPvzwQ9jZ2ckdjYj+ZTXPiRG1UcXFxRg7diwmTJiAMWPG4OLFi/j4449lK5cVFRWy7Le17P92rS3Po0KpVOL111/Hr7/+ik8//RSff/45+vbti+PHj8sdjYh+hwWTqA3au3cvAgMDcerUKSQlJSE+Pl62xzOuW7cOgwcPRs+ePc3LUlJSoNFokJeXJ/n+bt/26tWrER4eDo1GI/m+HsbdjkdLas5j35JUKhXefvttnD59Gr6+vggPD8e8efNQX18vdzQiAgsmUZvzP//zPxg7dizGjh2L7OxsDB06VNY8U6dOhclksvg//rKyMly6dAmVlZUN3k5RUVGD1rt922+99RbKy8thMpkaF7yJOe7lbsejJT3Msb+lqWNvDp6envj222/x+eef49NPP0VUVBSqqqrkjkX02GPBJGpDVq9ejX//939HXFwc1q9f3yqe+axQKODp6WmxLDo6GoWFhQgMDGzQNvR6PSZOnNigdW/ftlKpRNeuXRsXWoIc93K349GSGnvsb5Fi7M3pzTffREpKCtLS0vDyyy+jtrZW7khEjzUWTKI24ocffsDs2bPx0Ucf4T/+4z/kjiOZyspKjBs3TvZTuq0lhxwelbEPGDAA3333HQ4fPoyFCxfKHYfoscaCSdQG1NbWYsqUKRg9ejQWLFggdxzs3bsX06ZNw7x58zBr1qw7Tq3qdDqsWrUKGRkZ5mU///wzXn/9dcTFxeHdd9/F9OnTAQC7d+/G2bNnUVpaitjYWCxfvhyFhYX4+OOPERQUhGvXrmH48OHw8vLC1atX77rtW5KTkxEZGQkXFxcMHz4cFy9eBHDzSntHR0d069YNwM0bfS9ZsgQKhQIDBgy4Zw4AEEIgPj4e77zzDkJDQxEZGYnz58836ng8yJkzZ7Bw4UIEBASgsLAQY8aMgYuLC0JCQpCenm6xbmJiImbOnIm5c+di5MiRWLRoEaqrq+957E+dOoX33nsPvr6+qKysxNSpU+Hq6oqQkBDz8bnX2O/1O5NTaGgoPvvsMyxfvhw//fST3HGIHl+CiB55mzZtEjY2NqKgoEDuKGLr1q1Co9GIqqoqIYQQpaWlws3NTajVaiGEEGlpaSI8PFwAEImJiebP+fv7i7S0NCGEEEajUYSHh5t/NmrUKOHt7W1+/91334k//OEPQqFQiPfff198/vnnIiQkRHzzzTd33faIESNEp06dxJQpU8T3338v1qxZI+zt7YWHh4cwGAxCCCEiIyOFp6enxVh69eolNBrNPXMIIcSyZcvEpk2bhBBC1NXViYCAAKFWq0VlZWWDjkdDHDlyRAQEBAiFQiHmzJkjUlNTxc6dO0WnTp2Evb29uHLlihBCiBUrVoiwsDBRU1Nj3pefn58YNGiQMJlMdz32RUVFIiIiQgAQM2bMEDk5OeLkyZPCxsZGvPLKK/cd+/1+Z3LTaDQiKipK7hhEj6tVLJhEbcALL7wgXnzxRbljiMrKStGlSxexbds2i+Vjx461KFR///vfLUpOTU2NACA+++wz8zq/38bdys2bb74pAIgLFy5YLL9920LcLJgeHh4W6y1atEgAECtXrhRCCPHiiy/eUTA1Gs19C2ZhYaHo3LmzqK+vNy/705/+JACIr7/+usHHoyHeeOMNoVQqzeVRCCG2b98uAIg//elPoqSkRLRv315s2bLF4nMbN24UAMRXX311z+Mzf/58AUCUlpaalw0cOFD4+fndc+wP+p3JbePGjcLW1tZc9ImoRa3iKXKiNiAnJwehoaFyx8DRo0dRVFSEXr16WSy3tra2eG9vb2/xXqVSITIyErNnz8b06dOh1+sxYcKE++5LpVJBqVTiiSeeuO+2b3FycrJ4/8YbbwAAsrKy7ruf+zl+/Dhqa2vx1ltvITY2FrGxsbhy5QqmTp0KOzu7Bh+PhlAoFFAqlVCpVOZlUVFRsLa2RnZ2NtLT01FZWWk+zX/LqFGjAACHDx8GcPfjo1AoANy8IOoWT0/P+96r82F+Zy0pNDQUN27cMJ/mJ6KWpXzwKkTU2tXW1j5UaZHa2bNnAcCiBDXUrl27EBsbi7Vr12Lnzp3YsWMHBg0aJHVEM29vb1hbWzfplja//vor2rdvj/Xr19/15ytXrgTwcMejIVQqFTw8PFBXV4f8/HwAwLVr1yzWcXV1hb29Pa5cuSL5/lv6d9YYNjY2AGDx/VMiajmcwSRqA3x8fHDmzBm5Y5hL7q2y0xgqlQrbtm3Dli1bAACRkZHmwtoc2rVrB6VSiaCgoIfehr29PS5fvozLly/f8TOdTtek49FQNTU18Pf3h4+PDwDcc8bO399f8n239O+sMXJyctCuXTs+q5xIJiyYRG3Ac889h7179z7UzbOlFBwcDADYsWOHxfIH3Vi8uroaa9euBQDExMQgPT0dJpMJKSkpAG6WQYPBIGnWvLw81NbWYty4cQBunh42GAwWOQ0Gg8UN2m/P0atXLwghMG/ePItt//Of/8SaNWse+ng0lFarRXFxMaKjo6HRaODo6Ig9e/ZYrFNYWAij0YjRo0c3aV+3j/1BvzO5bd26FaGhoejUqZPcUYgeSyyYRG3A1KlTUVNTg48//ljWHGFhYRg8eDA2btyI+Ph4GI1G/Pjjj0hLS4NOp8O2bdtgNBpRUlICACgtLTV/dsOGDebS5enpCWdnZ/Tt2xcA4OHhgdLSUmRlZeGHH36A0Wg0l0G9Xm+R4W7bVigUKCsrMxdwIQSWLFmC999/H3/4wx8A3CyLer0ey5YtQ25uLj766CNUV1cjNzfXfLub23OEhYWhf//+2LZtG6Kjo5GQkIA1a9bgrbfewowZMxp8PBqquroa2dnZ5vdLly5FTEwMNBoNXF1dsWzZMhw7dgzJycnmdT799FNMnjzZ/ESnux2f8vJyAEBdXZ3FcayqqoIQ4p6/g/v9zuSUnp6OHTt2tKn7wRI9ahQffPDBB3KHIKKmsbe3h5OTExYuXIgBAwbcceFLS4qKikJxcTHWr1+P+Ph4ODg4oEuXLggODsaAAQNQWFiIv/zlL8jLy4NWq4WPjw+8vLywZcsW7N+/H4WFhUhISMDkyZMxZswYAED37t1x4MAB7Nu3DxqNBj/++CM+//xzVFZWori4GN7e3lCr1Th8+DDi4uIstu3r64ugoCBcvnwZX331FY4ePYrk5GRERETgrbfeMufu27cvcnJykJCQgOPHj2PWrFnQarXw9/eHl5cX/Pz87sjRp08f85NxDh8+jIMHD8LOzg6rV69G586dG3Q8/Pz8YGVl9cDjun//fmRnZ8NoNCIhIQGHDh2CWq3GihUrzJ8PCQlB7969sXLlSmRmZiIjIwMuLi6Ii4uDlZXVXY9Pfn4+li9fDr1eD4PBgJCQEOzZswfr1q1DRUUFrKysEB4eDi8vL4uxBwUF3fd3JhedToeRI0ciJCQES5cubdCxJSLJ/Wglbv3zlIgeeTExMdi7dy++++47DBw4UO44JKHY2FgkJCTwOdv3UVpaimeffRYVFRU4fvw43N3d5Y5E9LhazavIidqQjRs3Yvz48Xj22WfxxRdfYNKkSXJHogfw9PR84JXOX331VQuleXSdOXMGo0ePNn8PlOWSSF78DiZRG6JSqbBjxw5Mnz4dkydPxvjx46HT6eSORfdx+fJl6HS6+75GjhyJa9euoaamRvKLnR519fX1+Mtf/oJ+/fpBrVYjPT2dV44TtQIsmERtjEKhwF//+ld8//33SE9PR2BgIL755hu5Y1ETzJ8/HwcPHoTJZMKsWbOQlpYmd6RW4ddff8XAgQOxePFivP/++zh8+DBnLolaCX4Hk6gNu379OhYvXoxVq1YhJCQECxYswAsvvCB3LKImyc/Px3//93/jyy+/RGBgIDZt2oQ+ffrIHYuI/mU1ZzCJ2jAnJyesXLkSJ06cQMeOHTF69GgMHDgQhw4dkjsaUaNdvHgRU6ZMwZNPPonU1FRs2LABWVlZLJdErRALJtFjICQkBN9++625aEZGRqJnz5745JNPcPXqVbnjEd2TyWRCUlISxo0bB39/f6SmpmL16tU4c+YMXn31VfNz1ImodeEpcqLH0I8//oj4+Hhs374dQgiMGzcOsbGxePrpp+WORgQAKCgowJdffokNGzagsLAQQ4cOxbRp0zB27FgolbwBClErt5oFk+gxdv36dXz99deIj4/HyZMn0b17d7z44ot4+eWX8fTTT6NdO57koJZTUFCA3bt3Y8eOHThx4gScnZ3x8ssvY9asWQgMDJQ7HhE1HAsmEd2UlZWFxMRE7Ny5E+fPn0fXrl0RFRWFqKgoDBw4ENbW1nJHpDbo9OnT2L9/P3bu3ImsrCzzd4XHjh2LESNG8L87okcTCyYR3SknJwcHDhzA/v37cezYMdjb2+Ppp59GREQEIiIi8NRTT/ERfPRQtFotfvjhByQlJeG7777DpUuX0KlTJzz33HN4+eWXMXz4cJZKokcfCyYR3d/FixeRlJSEpKQkpKSk4OrVq+jcuTMiIiIwcOBADBgwAEFBQbzYgu6qoKAAJ06cwIkTJ5CUlIScnBzY2NhgwIABePbZZxEREYF+/frxvx+itoUFk4gazmQy4eTJk0hKSkJycjLS09NRUVEBBwcH/PGPf8TTTz8NjUaD0NBQ3vD6MVRVVYV//OMfyMjIwPHjx5GRkYErV65AqVQiODgYzzzzDCIiIjBo0CDY29vLHZeImg8LJhE9vPr6epw9exZZWVnIysrCsWPHcPLkSZhMJnTp0gWBgYEICAhAv379EBgYiKCgINjY2MgdmyRQVlaGnJwc8+/+zJkzOH36NKqrq6FWq/HHP/4R/fr1Q79+/RAeHo4OHTrIHZmIWg4LJhFJS6/XIzMzE6dOncLPP/+MX375BefOnUNtbS1sbW0RGBiI4OBg9OjRA35+fuaXnZ2d3NHpNvX19cjPz8f58+dx/vx5nDt3DmfOnMGpU6dw7do1AICXlxeCg4PNr9DQUHh5ecmcnIhkxoJJRM2vpqYGOTk5+OWXX/DLL7/g9OnTyM3NRUFBAUwmE6ysrNCtWzf4+fmZi6eXlxe6d++Obt26oXPnznIPoc2qqKhAQUEB8vPzUVBQgH/+85/mMnnx4kXU1NQAANzc3NCjRw8EBARYFErOTBLRXbBgEpF8qqurceHCBeTm5ppnyW69ioqKcOuvJ1tbW3PZ7NatG7y8vODp6YnOnTvDzc0NXbp0gbu7O2dBf6eurg5arRY6nQ5FRUXQ6XS4cuUKLl26ZC6TBQUF0Ov15s906NABTzzxhLno3yr7PXr0YJEkosZgwSSi1qmmpgaXLl3CpUuXLGbYbr2/dOkSDAaDxWccHBygVqvNxdPNzQ0uLi5wdnZGhw4d7vlqzcW0rq4Oer3e4lVWVmb+c3l5OfR6Pa5du4aioiJzqdTpdBbbsbGxgVqtRvfu3S1mh3//3tHRUaZRElEbw4JJRI+uqqoq8wzd7bN1t97fXsbq6uruui0nJycoFAp07NgRCoUCTk5OsLa2Rvv27WFnZwdbW1sAgEqlgoODwx2fd3Z2vuPJR9evX0d9fb3FsurqahiNRgA3v+N4/fp11NTUoLKyElVVVbhx4wYMBgNqa2tRXl4Ok8l017wdOnSwKM4dO3Y0z+S6ublBrVbD3d0d7u7uUKvVcHZ2bvTxJSJ6SCyYRPR4MRgMd8wIVlVVoaKiAnV1dSgrKzMXv1tl0Gg0orq6GgDMJfD3TCYTysvLAQCFhYVwcXGBnZ0d7O3t77hq/lZ5BQArKyt06NDBXFpvFVkHBweoVCrzz26fce3YsWMLHCkioofGgklEJJX6+noolUokJiYiOjpa7jhERHJZ3e7B6xARERERNRwLJhERERFJigWTiIiIiCTFgklEREREkmLBJCIiIiJJsWASERERkaRYMImIiIhIUiyYRERERCQpFkwiIiIikhQLJhERERFJigWTiIiIiCTFgklEREREkmLBJCIiIiJJsWASERERkaRYMImIiIhIUiyYRERERCQpFkwiIiIikhQLJhERERFJigWTiIiIiCTFgklEREREkmLBJCIiIiJJsWASERERkaRYMImIiIhIUkq5AxAR0aOtoqICjo6OjV5Hq9UiKSkJOp0OERERCAwMbM6YRNSCOINJRNSKpaSkQKPRIC8vT+4od1i3bh0GDx6Mnj17Nnqdc+fO/T979x0W1bX2D/87MAMKCChFQIqgSCiCKFIE1CAQUexisMSYc9C0Y8l5kp+JaSYmJ8YnMYnR2J4UayJghSRGsNBBHESwIChBelP60GZmvX/4so8jKCAzDOX+XNdcwJ49e91r1l7se6/dEBISAm9vb1y8eBHOzs6ora1VdMiEkF5CCSYhhPRhVVVVKCgoQENDQ7c/W1JSooCI/iskJARSqRQSiaTb82zatAkODg4wMzPDgQMH8Msvv0BbW1uh8T5O0d8PIYMZJZiEENKHLVq0CEVFRd0+fFxdXY1ly5YpKKqHVFVVYWpq+kzz/PXXX9DR0QEA6OjoKDzWx/XG90PIYEYJJiGEDDANDQ1YsmRJnzysDgCNjY3PNCIrL339YNXMxAAAIABJREFU+yFkIKCLfAghRM4KCwvx/vvv4+TJk4iKisIbb7yBuLg4jB07Fjt27IC7uzs3b3h4OGJiYqCuro4bN25g0qRJ+PDDD6Gurg4AqKiowLFjxzB58mS4ubkhPT0dR44cwfHjx5GZmYn169fj1KlTsLKywm+//QYrKyucPHkSWVlZqKqqwurVq2FjY4O3334b165dwzfffAM7OzuUlJSgubkZP/zwQ7fqdvr0afz+++8YPnw4GhsbOzzM/LR5Dhw4gOjoaABAWFgY7ty5g7Fjx2Ljxo1djuFp39mxY8cQEhICXV1dFBQUoKamBjt27MDmzZvh6uqKpKSkJ34/hBA5YoQQQuRCLBYzAGzLli3Mzs6Oqaqqsg0bNrCLFy+y48ePMz09PaahocGKi4sZY4xt376deXp6spaWFsYYY5WVlcza2ppNnTqVSaVSFh8fz7y9vRkAFh4ezhhjrKSkhPn6+jIA7M0332Q3btxgV69eZerq6iw4OJiLJTAwkI0ePVomPhsbGxYfH88YY0wkEjFvb+9u1e/IkSPM3d2dNTY2cvEaGBgwIyOjbs1TWVnJALDPPvusW+Uz1vl3xhhj/v7+zNTUVOZz48ePZ+7u7tzfHX0/hBC52UmHyAkhRM5sbW3h5uYGHo+Hbdu2Yfr06Vi4cCF++OEHiEQi7NmzB+Xl5fjwww/x2muvQSAQAAD09PSwadMmxMbG4vDhw/D09MSHH34os2wjIyNMnjwZAPDJJ5/Azs4OEyZMwOTJkyEUCp8YU2trK27fvo2rV68CAIYOHYrXX3+9y3USiUR4++23sW7dOgwZMoSL19vbu1vz9ERXvjMA0NDQaPdZTU1NucRACOkaSjAJIUQBVFVVwefzuUQIABYsWAA1NTVkZmYiOTkZDQ0NMDMzk/lcYGAgAODSpUsAOk6WVFVVAQB8/n/PcjI1NUVdXd0T4xEIBPD398f69evxxhtvoLq6GkuXLu1yfeLi4lBSUoLx48fLTFdTU+vWPD3R1e+MEKJ8lGASQkgvEQgEMDExgVgsxr179wAADx48kJlHX18fGhoaKC4ulnv5J06cwIsvvojdu3fDxsYGsbGxXf5sVlYWAMgkzM8yT08o4zsjhDwbSjAJIaQXtbS0wMbGBpaWlgCA3NzcDuezsbGRe9kCgQBHjx7FoUOHAAD+/v5cUtiZtlHItiTvWefpCWV8Z4SQZ0MJJiGE9JLy8nKUlpZi0aJFcHd3x7Bhw3Dq1CmZeYqKiiASiTB37twelaWiooL6+nru7+bmZuzevRsAsGLFCiQnJ0MqleLChQtdWp6joyOAh1d+P+rRm6h3ZR4AYIx1szYPdfU74/P5qK+vlymzvr4eUqmU+/vx74cQIl+UYBJCiII0NzcjMzOT+/vzzz/HihUr4O7uDn19fXzxxRdISEjA+fPnuXl27NiBl156CT4+PgCAsrIyAEBlZSU3T01NDQBALBZz08rKytDY2MglbyYmJqisrIRQKERMTAxEIhF+/PFHLukyNTWFjo4OnJ2du1QXT09PTJs2DT///DP27NkDkUiE1NRUxMfHo6KiAkePHoWzs3On84hEIuTn5wN4eFFQd3T1Oxs/fjyqq6vxxRdfIDs7G5999hmam5uRnZ2NtLS0J34/hBD5Ud28efNmZQdBCCEDAWMMn376KZYsWYI7d+4gMzMTIpEIhw8fRlRUFIyMjLB9+3bweDwAgKurK5ycnPDdd9/h8uXLSElJwYgRI7Bt2zbweDxcunQJ27ZtQ15eHsrLy2FpaYl79+7hq6++QnV1Nerr6+Hq6opTp05h7969qKurA4/Hg7e3NywsLBAZGYkzZ87A3d0dDg4OOHToECIiIlBUVITDhw/jpZdewrx587pcvwULFqC0tBT79+/Hnj17oKWlBWNjYzg6OsLDwwPW1tZYuHDhU+epr6/H9u3bkZmZicLCQhgYGMDc3Jy76rwznX1nAODs7IwbN27g8OHDSExMxLp161BeXg4bGxtYWFjA2toa5ubmMt+Pk5NT9xucEPIkqTz2rMcqCCGEyJBIJODz+QgPD8fZs2dx+PBhNDY2KjssQgjpbbvoST6EEDLImZqaorm5+anzHDx4EAEBAQM6BkKI/FCCSQghCvDgwQO0tLSgvr4eWlpayg7nqQoLC5UdQp+IgRAiP3SRDyGEyNmRI0fw119/QSqVYt26dYiPj1d2SIQQ0qtoBJMQQuRs+fLlOHHihLLDIIQQpaERTEIIIYQQIleUYBJCCCGEELmiBJMQQgghhMgVJZiEEEIIIUSuKMEkhBBCCCFyRQkmIYQQQgiRK0owCSGEEEKIXFGCSQghhBBC5IoSTEIIIYQQIlf0JB9CCHlGv//+O0QiEfe3VCqFpqYmUlNTIZVKZeb19fXF8OHDeztEQghRCh5jjCk7CEII6Y+WLVuGX3/9tdP5tLW1UVZWhiFDhvRCVIQQonS76BA5IYQ8o6VLl3Y6j0AgwOLFiym5JIQMKpRgEkLIM5o5cya0tbWfOk9rayuWLVvWSxERQkjfQAkmIYQ8I4FAgCVLlkAgEDxxHj09PUyfPr33giKEkD6AEkxCCOmBZcuWobW1tcP31NTUsGLFCqiqqvZyVIQQolyUYBJCSA9MmzYNhoaGHb7X0tLSpfM0CSFkoKEEkxBCekBFRQUrVqyAmppau/dMTU3h6uqqhKgIIUS5KMEkhJAeWrp0KVpaWmSmqampYdWqVeDxeEqKihBClIcSTEII6SEXFxdYWlrKTGtpaUFwcLCSIiKEEOWiBJMQQuRg5cqVMleT29rawt7eXokREUKI8lCCSQghcrB06VLuanKBQICXX35ZyRERQojyUIJJCCFyYGNjg/HjxwMAxGIxHR4nhAxqlGASQoicrFy5EgDg6uoKCwsLJUdDCCHKQwkmIYTIibGxMXg8HsaOHQuRSKTscAghRGl4jDGm7CAIIaS/++c//4mff/4ZPB4PKioqMDMzQ1paGnR1dZUdGiGE9LZdNIJJCCE9lJaWhp9++gmMMUilUojFYhQVFeGbb75RdmiEEKIUfGUHQAgh/VVubi6uXLmCgwcPQkVFBVKplHuvtbUVYWFhGD58OCZOnIgJEyZAW1tbidESQkjvoUPkhBDSBYWFhbhy5YrM6/79++Dz+bCwsMDdu3dl5ldTU4OlpSUePHiAiooK7tzMiRMnyrxGjBihpBoRQojC7KIEkxBCHlNdXY3r169DKBQiISEB8fHxKCkpAfDwQp5JkybBy8sLnp6ecHZ2hqamJhYvXoyTJ0+Cz+eDMQY9PT1kZGTAwMAAxcXFEAqFEAqFuHnzJm7cuIGbN2/KLK/t5eLiAmNjY2VWnxBCeooSTELI4Nbc3AyhUIjk5GQkJiYiNTUV+fn5AAArKyu4uLhwr0mTJj3xMLdUKkVoaCg2btyIuXPnYsuWLU+9wKesrAxpaWkyr7y8PACAiYkJXF1d4ebmBnd3d7i4uEBLS0vudSeEEAWhBJMQMrgUFhYiKSmJe6WlpaGlpQWGhoZwd3eHq6srXFxcMHny5G4fvpZIJODz+QgPD8eiRYu6HduDBw+4ZDMlJQUpKSkoKiqCqqoq7O3tuYTTzc0Ntra2UFGh6zQJIX0SJZiEkIFLLBYjLS0NiYmJXEJZUFAAPp8PBwcHTJkyBe7u7vDw8MDYsWN7XF5PE8yOFBYWIiUlBcnJyUhJSYFQKIRIJIK2tjYmT54Md3d3TJkyBZ6entDR0ZFLmYQQ0kOUYBJCBo7W1lZkZGQgOjoa8fHxiI+PR3V1NbS1teHq6gpPT09MmjQJ3t7eCrk/pSISzMeJxWJcv34dSUlJuHz5MpKTk5GVlQUVFRU4ODhg6tSp8PT0hLe3N0aNGqWQGAghpBOUYBJC+q/6+nokJycjPj6euxinqakJxsbG3EU4Xl5emDhxIng8nsLj6Y0EsyO1tbW4fPkyl1hfuXIFzc3N7b4HZ2dnOqxOCOkNlGASQvqPiooKxMTEIC4uDrGxscjMzIREIoGtrS28vb3h7e2NqVOnwtzcXCnxKSvBfFxDQwOXeMfHxyM5ORn19fXQ19fHlClTMHXqVPj4+MDJyYkSTkKIIlCCSQjpuxoaGpCUlITo6GhER0fj6tWr4PF4sLGx4UbmfHx8YGpqquxQAfSdBPNxYrEYV69eRXx8POLi4hAXF4fKykro6elh+vTp8PHxgY+PD5577jllh0oIGRgowSSE9B1NTU1ISkrChQsXcP78eaSmpkIikcDBwQE+Pj6YMWMGpk2b1mefiNNXE8yO5Obmcol7VFQUqqurMXLkSEydOhW+vr7w8/ODpaWlssMkhPRPlGASQpRHIpEgPT2dO3cwJiYGdXV13LmDvr6+mDVrVp8ZoexMf0owH/VoO7S1RVNTE6ysrLjzN/tTOxBClI4STEJI78rLy8Off/6Js2fPIiYmBjU1NTAyMuJGKH18fDB69Ghlh/lM+muC+bjGxkYkJibiwoULuHDhAq5cuQKJRAInJycEBARg1qxZ8PDwgKqqqrJDJYT0TZRgEkIUq6WlBXFxcfjzzz/xxx9/4NatW9DS0sKMGTO4hNLe3l7ZYcrFQEkwH1dbW4vY2FicPXsWf/75J3JzczF8+HD4+/tj1qxZmDlzJgwNDZUdJiGk76AEkxAif2VlZfjrr78QGRmJc+fOoaamBlZWVvD19UVgYCD8/f2hrq6u7DDlbqAmmI9rO38zIiICUVFRaG5uhp2dHebMmQNfX19Mnz4dfD5f2WESQpSHEkxCSM+1ncMXERGByMhIpKWlYejQoZgyZQoCAwMxf/58WFhYKDtMhRssCeajRCIREhMTERERgdOnT+PevXvQ09ODj48PfH19MXfuXBgZGSk7TEJI76IEkxDybCorK3HmzBlERkYiOjoadXV1eO6557hDplOnTh2Qo5RPMxgTzMddv34df/zxB/78808kJCRAKpXC3d0d8+bNw4IFC+TySE5CSJ9HCSYhpOsKCgpw6tQpnDx5ErGxsVBTU8OMGTMQEBCAgICAQX9bG0owZdXU1CA6OhqRkZGIjIxEZWUlHBwcMH/+fCxYsAATJ05UdoiEEMWgBJMQ8nR///03zpw5g7CwMCQmJmLo0KHw8fFBUFAQFixYgGHDhik7xD6DEswnk0gkiI2NxalTp3Dq1Cnk5+fDwsIC8+fPx6JFi+Dp6UlPFSJk4KAEkxDS3o0bNxAWFobIyEgIhULo6elh1qxZCAoKGrAX6MgDJZhdJxQKcerUKZw4cQI3b97EqFGjsHjxYixZsgQeHh698ux4QojCUIJJCAGkUimuXr2KiIgI/Prrr8jOzoaZmRkCAgIQGBiImTNnQiAQKDvMPo8SzGfTtkMTGhqKW7duwdTUFAsXLkRQUBA8PT0p2SSk/6EEk5DBijGGpKQk/PrrrwgLC0NZWRmee+45LFiwAAsXLsSkSZNow95NlGD2XFuyeezYMWRlZcHMzAzLli3DK6+8AhsbG2WHRwjpGkowCRlsrl+/jqNHj+LXX39FXl4e7OzsEBwcjEWLFsHOzk7Z4fVrlGDKV3p6Oo4cOYKjR4+iuLgYbm5uWLFiBYKDg6Gvr6/s8AghT0YJJiGDQUFBAQ4fPoxff/0VmZmZMDc3R3BwMJYtWwYnJydlhzdgUIKpGBKJBBcuXMDhw4dx4sQJNDc3Y+bMmVi5ciXmzp0LNTU1ZYdICJG1iy7ZI2SAamxsxNGjR+Hv74/Ro0dj+/bt8Pb2RlxcHPLy8vDll19Sckn6BVVVVfj5+eHAgQMoLS3FTz/9hJaWFgQHB8PMzAzvvPMOsrKylB0mIeQRlGASMsAIhUKsX78eZmZmWLlyJRhj+O2331BUVIRdu3bBy8uLzq0k/ZampiZWrFiBs2fPIj8/H//+979x/Phx2NrawsXFBfv27UNDQ4OywyRk0KMEk5AB4P79+/j666+5jeyFCxewadMmFBcXIyoqCkFBQXQYkQw4JiYm2LhxI+7cuYM///wTo0ePxtq1a2Fqaoq1a9fi9u3byg6RkEGLEkxC+rH4+HisWLECo0aNwpYtW+Dj44PU1FRkZmbi3//+NwwNDZUdIiEKp6KigpkzZyI8PBwFBQXYtGkT/vjjD9ja2uKFF15AZGQkpFKpssMkZFChBJOQfqa2thb79u3DhAkT4O3tjaysLOzYsQOFhYXYtWsXXFxclB1ir6mrq2s3rby8HEePHsV3332HGzduKCGqwacvtYOhoSHeeecd5OTk4Ny5c1BTU8PcuXNhbW2NL7/8Evfv3++1WEjH+tL6QhSHEkxC+gmhUIhXX30VJiYmWL9+PcaNG4f4+HhcuXIFa9asgZaWllzLu3DhAtzd3ZGXlyfX5crD3r17MW3aNNja2spMv337NkJCQuDt7Y2LFy/C2dkZtbW1nS6vL9e1L8fWl9tBRUUFvr6+iIiIwO3btxEUFIQvv/wSpqamWLlyJTIyMnpcRl9E6wvpKyjBJKQPa2xsxP79+zFx4kS4uLggOTkZ27ZtQ1lZGUJDQ+Hp6amwsquqqlBQUPBMF0yUlJQoIKL/CgkJgVQqhUQikZm+adMmODg4wMzMDAcOHMAvv/wCbW3tTpfXk7oqGrVDz1lbW2Pr1q3Iy8vDtm3bcPnyZTg5OWHGjBmIjIzEQLpbH60vpM9ghJA+Jz8/n7377rtMT0+PDRkyhL388sssMTFR2WF1SVVVFZs+fbrCywkODmZGRkYy0zQ1NdnWrVsVXvaTiMViBoCFh4crLYY2g7kdOiOVStnZs2fZrFmzGI/HY3Z2duzHH39kTU1Nyg5NaWh9IXK2k0YwCelDhEIhVq5ciTFjxuCXX35BSEgI7t69i19++QUeHh7KDq9TDQ0NWLJkiVIOWTU2NtJIxv+P2uHpeDweXnjhBfz++++4ffs2fH198eabb8Lc3BybN2/GgwcPlB1ir6L1hSgCJZiEKFlLSwvCwsIwZcoUuLi44ObNm9i5cyf+/vtvbN26FSYmJl1e1s2bN/H+++/Dzs4ORUVFmDdvHkaMGAFXV1ckJyfLzBseHo61a9fi7bffRkBAAD744AM0Nzdz71dUVGDnzp1ISUkB8PCxfe+88w6srKzQ0NCAkJAQ6Ovrw9XVFbm5uQCAkydPIisrC5WVlVi9ejW++uorAMC1a9ewatUqbNu2DW+99RbeeOONbn9Pp0+fxpo1a7Bx40asW7dO5nDegQMHsGbNGgBAWFgYVq9ejS+//LLLy36Wuj5NYWEhtUMfaIeusLa2xnfffYe8vDy8/vrr2LFjBywsLLB+/Xrk5+fLpYzOUL/tP+sL6QZlj6ESMliVlpayrVu3slGjRjGBQMCCgoJYVFRUj5YZGxvL7OzsmKqqKtuwYQO7ePEiO378ONPT02MaGhqsuLiYMcbY9u3bmaenJ2tpaWGMMVZZWcmsra3Z1KlTmVQqZfHx8czb21vmcG9JSQnz9fVlANibb77Jbty4wa5evcrU1dVZcHAwF0NgYCAbPXq0TFw2NjYsPj6eMcaYSCRi3t7e3arXkSNHmLu7O2tsbOTiNTAwkDnUVllZyQCwzz77rFvL7kldH9d2iHzLli3UDkpsh56oqalhX331FTM1NWUCgYCtWLGCpaenK6SsNtRv++/6Qp5oJyWYhPSy7Oxstm7dOjZkyBBmYGDANm7cyPLz8+W2/FdeeYXx+XxuI8QYY8eOHWMA2EcffcTKysqYpqYmO3TokMznfv75ZwaAHTx4kDHG2Llz59qdT/jee+8xAKyyspKb5uXlxaytrbm/H99QtbS0MADs+++/56YdPXq0y/VpaGhgxsbG7T6zcOFCuWyoGHv2uj7u0XMwqR2U1w7y0NLSwkJDQ9nkyZMZAObr68uSkpIUVh6tL/17fSHt0DmYhPSWS5cuYfbs2bCxsUFUVBS+//575OfnY+vWrTAzM5NbOaqqquDz+RAIBNy0BQsWQE1NDZmZmUhOTkZDQ0O7MgMDA7k4AUBDQ6PDZQMAn8/nppmamnZ4X7s2AoEA/v7+WL9+Pd544w1UV1dj6dKlXa5PXFwcSkpKMH78eJnp8nwykbzq+vjnqR26RxHt8KwEAgGCgoKQkpKCyMhINDQ0wMPDA/7+/oiJiZF7ebS+dF9fWl9Ie5RgEqJAUqkUERER8PDwwPPPP4+amhocO3YMmZmZCAkJwZAhQ3olDoFAABMTE4jFYty7dw8A2l3IoK+vDw0NDRQXF8u9/BMnTuDFF1/E7t27YWNjg9jY2C5/NisrCwBkNrz9FbVD/8Pj8TB79mwkJiYiLi4O6urqmD59Ory8vBAREaHQWxzR+kL6M0owCVGAhoYG7Nu3D7a2tpg/fz709fWRmJiI+Ph4BAUFcXvZvamlpQU2NjawtLQEgCee9G5jYyP3sgUCAY4ePYpDhw4BAPz9/bkNUGfaRjzaNrD9HbVD/9WWVMbHx2P48OGYN28eJk6ciLCwMIUlmrS+kP6KEkxC5KisrAwffPABzM3N8dZbb8HPzw/Z2dncKKaylJeXo7S0FIsWLYK7uzuGDRuGU6dOycxTVFQEkUiEuXPn9qgsFRUV1NfXc383Nzdj9+7dAIAVK1YgOTkZUqkUFy5c6NLyHB0dATy8yvRRj9+wWZEjSfJC7TAweHp6IiIiAikpKTA3N8eLL74IZ2dnhIeHy7X+tL6Q/owSTELkID8/H+vXr4eVlRX27duHtWvX4t69e9i5cyfGjBnT6/E0NzcjMzOT+/vzzz/HihUr4O7uDn19fXzxxRdISEjA+fPnuXl27NiBl156CT4+PgAeJssAUFlZyc1TU1MDABCLxdy0srIyNDY2chsKExMTVFZWQigUIiYmBiKRCD/++CO3UTE1NYWOjg6cnZ27VBdPT09MmzYNP//8M/bs2QORSITU1FTEx8ejoqICR48ehUgk4m4pIxKJuv19PWtdO0Pt0D2KagdFmTx5Mk6fPo309HSMGzeOSzRPnz79TLHR+tI9/W19GWxUN2/evFnZQRDSX+Xm5mLz5s1YtWoVCgsL8cEHH+DQoUPw8/Pr8AT03hAREYHMzEyIRCIcPnwYUVFRMDIywvbt28Hj8QAArq6ucHJywnfffYfLly8jJSUFI0aMwLZt28Dj8XDp0iVs27YNeXl5KC8vh6WlJe7du4evvvoK1dXVqK+vh6urK06dOoW9e/eirq4OPB4P3t7esLCwQGRkJM6cOQN3d3c4ODjg0KFDiIiIQFFREQ4fPoyXXnoJ8+bN63KdFixYgNLSUuzfvx979uyBlpYWjI2N4ejoCA8PD9TX12P79u3IzMxEYWEhDAwMYG5u3qVzXHtSVxUV2X10xhg+/fRTLFmyBHfu3KF2UFI79LaRI0ciKCgIQUFByMnJwQcffIDjx49DX18fdnZ2XHs/DfXbwbO+DBKpPEYpPSHdlpmZif/93//F0aNHYWFhgXXr1uG1116Durq6skPD6tWrcfjwYTQ2Nio7lEFHIpGAz+cjPDwcZ8+epXYYpK5fv45PP/0U4eHhcHV1xfvvv485c+Y89TPUb8kAs4vf+TyEkDZpaWnYunUrwsPD4eDggJ9++gnLly9XykU7A4GpqanMU0g6cvDgQQQEBPTJ5Q8U1A7y5eDggNDQUGRkZOCzzz7D3LlzMWXKFHz66aeYMWOGssPrMVpfSFdQgklIF0RHR+Pzzz/HpUuXMGXKFERERGDWrFldOvTV2x48eICWlhbU19dDS0tL2eE8VWFhYb9e/tNQO/Te8vsqR0dHhIaGIikpCR999BF8fX3h4+ODzz77rN1Ff7S+9N7ySe+gExEIeYpz587B09MTfn5+EAgEuHjxIhISEjB79uw+mVy+9957+OuvvyCVSrFu3TrEx8crO6RB6ciRI9QOhOPh4YGoqCjExMRALBZjypQpmD9/Pm7cuAGA+i0ZmOgcTEI6EB8fjw8//BCXLl2Cr68vtmzZAnd3d2WHRfq4R8/BXLRokbLDIX1UdHQ0Nm7ciPT0dCxatAhbt26FlZWVssMiRJ520QgmIY+Ijo6Gu7s7vL29wefzkZycjKioKEouCSFy4+vriytXruC3335Deno6bG1t8eqrr6K0tFTZoREiN5RgEoKHiaWbmxv8/PwwbNgwpKSkICoqCm5ubsoOjRAyAPF4PAQFBeHGjRv4/vvvERERgbFjx+Ldd9/l7uVISH9GCSYZtBhjOHPmDFxcXODv7w9jY2MIhUJERUXB1dVV2eERQgYBgUCANWvWICcnB5s2bcLevXthbW2NHTt2oKWlRdnhEfLMKMEkg9LZs2fh5uaG+fPnw9zcHGlpaTh16hQmTpyo7NAIIYOQpqYmNm3ahLt372LVqlXYuHEj7O3t5f74SUJ6CyWYZFBJSEjA888/j4CAAOjo6CA1NRUnTpzAhAkTlB0aIYRwT+bJycmBj48PgoOD4e7ujtjYWGWHRki3UIJJBoXk5GTMmTMHXl5eaG1tRUxMDKKiojBp0iRlh0YIIe2Ymppi7969SElJgaamJqZNm4Y5c+YgJydH2aER0iWUYJIBLTMzE0uWLIGHhwcePHiA8+fPIz4+HlOnTlV2aIQQ0qlJkybhwoULiIqKQn5+Puzt7fHqq6+irKxM2aER8lSUYJIBKTMzEwsXLoSTkxPy8/Nx7tw5JCQkwMfHR9mhEUJIt/n6+kIoFGLnzp04c+YMbGxs8OWXX3b6SEVClIUSTDKgFBQU4NVXX4WzszNycnJw7NgxJCUlwc/PT9mhEUJIj/D5fO6K8/Xr1+PTTz+FnZ0dTp48qezQCGmHEkwyIFRUVGDDhg2wtrbGxYsXceTIEWRkZCAoKKhPPtKRDAx5eXnIzc2VeQFAWVlZu+lNTU1KjpYMFFpaWvjkk0+QnZ0NT09PLFq0CD4+PsjIyFB2aIRw6FGRpF9raGjAzp078cUXX0AgEODtt9/Ghg0boK6uruzQyCDg5+eH6OjoTucTCARN/aRGAAAgAElEQVQoLS3FiBEjeiEqMtikpqZi/fr1SElJwfLly/H111/DwMBA2WGRwY0eFUn6p9bWVuzbtw/W1tb47LPP8Nprr+Hu3bvYuHEjJZek1yxdurTTEXIVFRX4+flRckkUZvLkyUhISMDPP/+MqKgo7vxMulE7USZKMEm/whhDWFgY7OzssHbtWsyZMwd37tzB1q1boa2trezwyCCzePFi8Pn8Tud76aWXeiEaMpjxeDysXLkSd+7cwbp167B582Y4Ojrijz/+UHZoZJCiBJMoXVxcXJfmi46OhouLC4KDg+Hs7Ixbt25h7969GDlypIIjJKRj2traCAgIeGqSqaamhjlz5vRiVGQw09TUxObNm5GZmQlHR0fMnj0bfn5+uHXr1lM/19raioMHD/ZSlGQwoASTKNWBAwfw/PPPIz09/YnzpKamYsaMGdxhRqFQiNDQUFhZWfVipIR0bMWKFZBIJB2+x+fzsWDBAmhqavZyVGSwGzt2LEJDQ3H+/HmUlZXByckJ69evR21tbYfz79ixA6tWrcKJEyd6OVIyUFGCSZTmr7/+wj//+U9IpVJs2LCh3fvZ2dlYsmQJ3Nzc0NDQgIsXLyIqKooe60j6lDlz5kBDQ6PD98RiMZYvX97LERHyXz4+PkhLS8POnTvx66+/YsyYMfjuu+9kdoqKiorw4YcfgjGG5cuXP3WHn5CuogSTKEVmZiYWLVoExhgYY4iJicHvv/8O4OEth9avXw97e3tcv36du5fl9OnTlRs0IR0YMmQIFi5cCIFA0O49bW1tugcrUbq2+2fevn0by5Ytw9tvvw1XV1fEx8cDADZs2ACxWAzg4aHyWbNm0ZOCSI9Rgkl63d9//w0fHx80NzdDKpUCeHil7dq1a/Huu+9i9OjROHnyJPbt24fMzEy6lyXp85YtW4bW1laZaQKBAEuXLoWampqSoiJE1vDhw/Hdd98hLS0Nurq6mDp1KmbOnInw8HBu/ZVIJKisrMScOXPoKUGkR+g+mKRX3b9/H25ubrh37x63x9xGRUUF+vr6+Pe//41169Zh6NChSoqSkO4Ri8UYOXIkHjx4IDM9JiaGnntP+qzQ0FC8+eabqKqqanceMZ/PR3BwMA4dOqSk6Eg/R/fBJL2nsbERs2fP7jC5BACpVIqWlha89tprlFySfqVtY/zoYXIDAwN4eXkpMSpCnu7u3bsdJpfAw52mI0eOYPv27UqIjAwElGCSXiGVSrF06VIIhcIOk8s29fX1+PLLL3sxMkLkY+nSpdxhRjU1Nbz88stQUaF/saRvKigowKeffvrEOyAAD+87/PbbbyMyMrIXIyMDBR0iJ71i7dq12L1791P/mbURCATIycmBhYVFL0RGiHwwxjBq1CiUlJQAAK5cuYJJkyYpOSpCOjZv3jz8+eef7c4dfpyKigqGDh2K1NRU2Nra9lJ0ZACgQ+RE8bZu3Ypdu3Z1KblUUVFBa2sr3n///V6IjBD5aXuSCgBYWVlRckn6rPj4eJw5cwatra1QU1N76kWUUqkUzc3NCAgIaHeOMSFP0/kzzsgzk0qlqKmpQXV1NWpra1FbW4vm5mbU1tZCIpFwP2tqaiCVSlFdXQ3GGPcTgMzvjxKJRB1e4cfj8aCrq9thPMOGDeOeONL2e9tPbW1tqKqqQkdHByoqKtDR0QGfz4eOjg50dXWhra0NbW3tbj/n+/Dhw9i0aVOHdeDz+ZBIJGCMQVVVFRYWFpg4cSImTJgABweHbpVDSHeIxWJUV1fLvBobG9HU1IS6ujqIxWJUVVWhtbUV9fX13HsAuP76+PLq6upQV1fHTfPz88OQIUM6PJ+4rb+pqqpy/UpDQwNaWloQCATQ1dXl+qWmpiZ0dXW5F52fTHrKy8sLNTU1yMjIgFAoRFxcHGJiYlBZWQkejwc1NTWZ7YtYLEZxcTHmzJmDixcvduvOCG3bwLZXc3Mzampq0NLSgoaGBm5bVltby/VLAFzfe9yTtn2Pbt/aPLo9FAgE0NLSwtChQzFkyBBu/uHDh3PbQupr8kWHyLugubkZlZWVqKysRFlZGfd7ZWUlKioqcP/+fVRVVXFJZNuro87xqEeTuY5+Ag8f+9VRZ1ZTU+vw6SBtnbYjVVVV3O+PJ7mP/3wSdXV1LtnU1dWFjo4O97uBgQEMDQ1hYGAAfX19/P3339iwYQMkEglUVVUhkUjA4/FgZGQEZ2dnODo6wtHREfb29njuuefodi7kmd2/fx9lZWWoqKhAUVERKioqUFZWhpKSEjx48ABVVVUyG7lHE8HHdbTheTRR1NDQ6HBHa/jw4QCA8PBw+Pn5QUdHB/X19e0OQbbtRAL/TUybmprQ2NjIzf+kHUvg4X03H90IDh8+HCNGjICBgQGMjY0xcuRIGBoawsTEhOuPXXleOiH5+fm4fPkyLl++jKSkJKSlpUEkEkFVVRWMMe5c+tdffx1lZWUoLS1FRUUFSktLUVlZKdPH2vrck9bjtm1YW39q63e6urrg8XhPHCxp2xl7XEdlPZqkNjc3QyQSySS0EolEZrvYUVmP9jVdXV2ur7X1MQMDA4wcORLGxsYwMDDo9iDMALZrUCeYzc3NKCkpQWFhIYqKilBSUoL8/HxuWmlpKcrKytptjAQCAfT19WVew4cP5xKvR18djQA+mkD2VbW1tWhtbW03Avvoq7q6GjU1NaitrUVVVRUqKipQXl6OyspKiEQimeUJBAIMHz4cpqamMDU1hZmZGUxMTGBqagoTExOMGjUKpqamGDZsmJJqTPqqqqoqFBQU4N69e7h37x7y8/O5V0FBAcrLy9HS0sLNz+fzZTYAenp6XCL2aFL2+IajbWSjp/78808EBAT0eDltO3wikUgmQe7o9/v376OiogLFxcUoLy9HY2MjtxwejwdDQ0OMHDkSFhYWsLCwgLm5OfeysLCAsbEx3Wt2kKuurkZ+fj7Xx9r63O3bt5Gfn4/q6mqZwQcejwcDAwMuwTI0NGy34/N4H9PV1cWQIUOgra2txJq219bXGhoa2h3ZePzV1tfadmAfH9DR1dWFsbExzMzMYGZmxvUxc3NzbtogSUIHdoIpFotRUFCA3Nxc3L17F7m5udyrsLBQ5kkFqqqqGDlyJMzMzLiVY+TIkTAyMpJJJA0NDbmRCvJkDx48QH19PTeq1DbiW1xcjJKSEhQUFKC4uBhFRUXcoUfg4eiRmZkZLC0tYWVlBSsrK4wZM4b7nQ5ZDEzV1dXIzs7G7du3kZWVhezsbGRnZ+Pvv/+W2cEzMDCQ+adtZmYGQ0NDGBkZcSMIhoaGgz5Zqq2tRUlJCcrLy1FaWorS0lIUFxfLJOrFxcVcwqCurg4zMzOMHTsWzz33HGxsbDBu3DiMGzcOpqamSq4NkZfKykqun2VnZyMnJwfZ2dnIy8uT6Wd6enpcQmRhYQFTU1MYGRnBwMAAenp6ePDgAWbMmNHh06sGG5FIxPWxiooKlJSUoKysDPfu3UNBQQHX59q2c21H8SwtLbl+Zm1tzfW3AZR8DowEs7y8HDdu3MCtW7dw48YN3LlzB7m5ubh37x53eEpbW1smUWnbSBkbG8PU1BQjR46EqqqqkmsyON2/f19m9Lhtp6Btx6C0tJSb19jYGGPGjOE2hHZ2drCzs4OlpWWfHxUmQF1dHTIyMnDt2jVcu3YNWVlZyMrKQnl5OYCHiY61tTX3j9fS0pIbaRs9ejTtYMiRWCxGUVER8vPzkZeXh3v37iEnJwe3b9/G7du3ucP4WlpaGDduHGxsbODo6IgJEybAyckJxsbGSq4BeZLKykquj2VmZnI7bW0X6QwdOpRLaKytrWFpaSmz49bRIWjSM2VlZVzCmZ+fjzt37nA70vn5+ZBKpVBRUYG5uTnGjRsHOzs7ODo6wsnJCfb29v0x8exfCWZVVRXS0tJw8+ZNmYTy/v37AB6eA2VrawsbGxuZka8xY8ZAX19fydGTZyUSibgR6LafOTk5yMrKQn5+PoCH/zBtbW1ha2sLe3t72NraYsKECRg9erRygx/EysvLcfnyZaSnp+PatWtIT0/H3bt3wRiDrq4uJkyYwPXXtoTSwsKCdvT6iPLycpnR5Fu3biEjI4Prc4aGhnBycuISThcXF9jY2Cg56sEnOzsbQqGQSygzMjJQXFwMADAyMoKjoyNsbW25hHLcuHEwMzMb9KP8fUlzczPXz9pGla9fv47r16+jsbERfD4fNjY2cHJy4nbyJk+ejBEjRig79KfpuwlmXV0drl27BqFQyL1u3brFbZzGjBkDOzs72Nvbcz8tLS2p0wwytbW1yMnJQW5uLm7cuMHtfGRlZUEqlUJHRwcODg6YNGkS97Kzs6P1RAFyc3MRHx+PhIQExMfHc/3V2NgYkyZN4voqtUH/VlNTg8zMTAiFQq6/CYVCNDU1QVtbG66urvD09MSkSZMwdepU6OjoKDvkAaOhoQFXr16FUChEQkICLl26hIqKCvD5fIwbN06mj7m4uNAo8wBQXFzM5UCPDq4xxmBlZcX1NS8vLzg7O/elI3l9I8FkjOHWrVuIi4tDXFwcLl++jDt37shsnFxcXLgEgToN6Ux9fT3S09Nx5coVCIVCXLlyBdnZ2ZBKpTAwMICLiwu8vLwwdepUTJ48uT8eflAqxhgyMjJw7tw5nD9/HklJSaitrcWwYcO4BMPDwwMeHh6UYAwCra2tSEtLQ1JSEhITE5GYmIiioiIIBAJMmDAB06dPh7+/P7y8vORyIdVgUV9fj5iYGERHRyMmJgaZmZkQi8UwNTXFlClT4OHhAXd3d0ycOJHuwjGIVFZWIjk5metvqampaGhogI6ODtzd3eHj4wNfX19MmDBBmQmnchJMiUSCjIwMxMbGIjY2FnFxcaioqICWlhamTJkCd3d3Lqk0MTHp7fDIAFVXV4erV6/iypUrSE1NRWxsLIqLizF06FC4urpi2rRp8Pb2hoeHR4e3gBrsSktLERUVhXPnziEqKgplZWUwMDDAjBkz4OXlBU9PT4wfP54OcRMAwL1795CQkICkpCRER0cjKysLGhoa8Pb2hr+/P/z9/el+t4+RSCS4cuUKoqKiEB0djaSkJLS2tmL8+PF4/vnnMWXKFEyZMoUuvCIyxGIxMjMzkZiYiISEBJw/fx7l5eUwMDCAj48P/Pz84OfnB3Nz894Mq/cSzAcPHuD8+fOIjo7G6dOnUVZWhmHDhsHNzQ2+vr7w9PSEq6sr7YWRXlVcXIyEhARER0cjPj4eN2/ehKqqKtzd3TFnzhzMmTMHdnZ2yg5TaQoKCnDixAmEhYUhMTERqqqqcHJygq+vLwIDAzFlypS+dEiG9GGlpaWIi4tDdHQ0IiIiUFJSAjMzMyxYsABBQUHw9PQclKdNSCQSJCUlISwsDMeOHUNZWRlGjhyJqVOnwtfXF7NmzaKEknRbbm4uoqOjER0djbNnz6Kurg52dnYICgrCkiVLemO7ptgE89atW4iIiMDvv/+OhIQEqKiowNvbG7Nnz4afnx/s7e1p40T6lKKiIly4cAF//PEH/vrrL1RVVcHGxgaBgYGYPXs2pk6dOuBH6AoKChAWFoawsDCkpKRAV1cX8+fPx6JFizB9+nQa3SU9JpVKIRQKcfr0aYSGhiInJwcWFhZYvHgxgoKC4ObmpuwQFaqlpQVRUVEIDw/H6dOnUV1dDRcXFyxevBiBgYGDeqeWyF9TUxPi4uJw6tQpnDhxAqWlpbC3t+f6m729vSKKlX+CWVJSgtDQUISFhSEhIQF6enrw8fFBYGAg5s6d+8THGBLS10gkEqSnpyMiIgKRkZEQCoUYMWIEFi9ejJdeegleXl7KDlFupFIpLly4gH379uHkyZPQ0tLCnDlzEBQUhBdeeIGOLBCFunHjBjeCl5WVBRsbG7zyyitYvXp1X79StlsKCwtx5MgR7Ny5E4WFhdyI0vLly2Ftba3s8MggIJVKkZiYiLCwMJw4cQKFhYWYNGkS1qxZg+XLl8tzAGEXmBw0NDSwQ4cOsRdeeIGpqqoyXV1dtnr1ahYbG8skEok8iiBE6XJyctgnn3zCrK2tGQBmb2/Ptm7dygoLC5Ud2jMrKytjn3/+ObOwsGA8Ho/5+vqy8PBw1tzcrOzQyCCVmprKQkJCmKamJtPS0mJr1qxhV69eVXZYz6y1tZUdP36c+fv7MxUVFWZqaso+/vhjlp+fr+zQyCAnlUrZpUuX2NKlS5m6ujrT1dVl//rXv9j169flsfidPUowKyoq2NatW5mxsTFTVVVlvr6+7MCBA6yhoUEewRHSZ125coWtW7eOGRoaMhUVFRYYGMguX76s7LC67P79++zjjz9mw4YNYzo6OmzNmjXy+qdCiFzU1NSwvXv3MkdHRwaA+fr6MqFQqOywukwikbDQ0FBmY2PDVFRUmK+vLwsNDWWtra3KDo2QdqqqqtjevXuZvb094/F4LDAwkKWlpfVkkc+WYN69e5e9+eabTENDg+nr67OPP/6YlZeX9yQQQvql5uZmdvDgQebo6Mh4PB574YUXWHR0tLLDeqKamhr28ccfM21tbWZoaMi2b9/ORCKRssMi5Kl+//13NmnSJKaiosKCg4NZVlaWskN6IolEwo4dO8bs7OyYqqoqW7lyJcvJyVF2WIR0iVQqZadOnWJOTk5MRUWFvfjii+zWrVvPsqjuJZj19fXs448/Zmpqamz06NHs22+/ZfX19c9SMCEDTlxcHAsMDGQ8Ho9Nmzatz40I/vHHH2zUqFFs2LBhbOPGjaympkbZIRHSLVFRUWzChAmMz+ezjRs39rlTOe7evcumT5/OVFRUWFBQ0LNumAlROqlUys6cOcOcnZ2Zqqoq27hxI2tqaurOIrqWYEqlUnb48GFmbGzM9PX12f79+5lYLH62qOWstrZW2SEozWCqe3+qa2JiIps0aRITCATs//2//8fq6uqUGk91dTVbtWoVA8Befvll9uDBA6XG05/aUt76e907ir+srIwdOXKEffvtt72yUyWRSNjOnTuZpqYmc3Z2ZhkZGQovsysxffvtt0xDQ4NNmDChp4cW5aK/r2s9MZjrLm8SiYTt2bOHDRs2jNnb23fnVLDOE8yysjLm5+fHVFRU2Guvvcbu37/fs2jlZM+ePWzq1Kls1KhR3LTz588zNzc39vfffysvsF6wc+dO5uXlxezs7JQdisL117qKxWL2ww8/sOHDhzMzMzMWHx+vlDjS09OZubk5MzIyYmfOnFFKDG2oz/a/9bhNR23HGGNZWVlszpw5LD8/n82bN48JBIJeGxnPyclhnp6eTF1dne3fv79XyuzI/fv3mY+PDxMIBGzz5s2spaVFabEwRv2sP/ezviwvL4/NmDGD8fl8tm3btq585OkJZnp6Ohs1ahSzsrJiKSkp8olSTsRiMfPy8mJGRkbctPDwcGZiYtKtveji4mJFhKdQra2tbPz48ey5555TdigK19/rWl5ezubOncsEAkGvbwRjYmLYsGHD2IwZM1hlZWWvlt0R6rP9dz3uqO0YY2zhwoXsvffeY4w9HCk/cuRIr8f10UcfMR6Px7Zs2dKrZTPGWFFREbOxsWHm5uZ9YtSSMepn/bmf9XVSqZR9/fXXTFVVla1du5ZJpdKnzf7kBDMtLY2NGDGC+fj4KP2Q2pMEBwe3+4fXHVVVVWz69OlyjKj3zJw5c9B0ov5eV6lUym0Ed+zY0StlXrt2jeno6LDFixf3qfPUqM/23/W4o7bT1NRkW7duVVJE/7V3717G4/HY999/32tlVldXMwcHB2Zra9vnkjHqZ/23n/UH4eHhjM/ns02bNj1ttp38ju6OWVZWhnnz5sHZ2RkREREYOnSovG682Wc0NDRgyZIlyMvLU3YoZIDj8Xj45JNPMHToUGzYsAFjx45FQECAwsoTiUR48cUXMWHCBBw5cmTA3CSd+mzf0tjYiIaGBmWHAQBYs2YNKisr8e9//xtubm6YPHmywstcvXo17t+/j8uXL8PY2Fjh5fUW6mekM4sWLcL+/fvxj3/8g3usckc6fE7j//zP/4DP5yMsLKxPJZenT5/GmjVrsHHjRqxbtw4lJSUy71dUVGDnzp1ISUnhpl27dg2rVq3Ctm3b8NZbb+GNN94AAJw8eRJZWVmorKzE6tWr8dVXXwF4mFyvXr0aW7ZswerVq7FgwQLcv38fAJCeno533nkHVlZWaGhoQEhICPT19eHq6orc3FyZWP744w+88cYbWL9+PTw8PLB//37uPcYY9uzZg9dffx1ubm7w9/dHTk7OM30n58+fh7+/P0aMGIEXXngBubm5OH36NIYNGwYej4dvv/0WLS0tAICkpCQYGxvjP//5T5eW3ZX6Hjt2DMOGDYOZmRkAoKamBlu2bIGqqio8PDwAPHxKx6ZNm2BjY4PCwkJs3rwZ5ubmsLe3x8WLF9HU1IS33noLY8aMgbm5Of76668u17XN09qtr3j33XexbNkyhISEoL6+XmHlfPPNNygpKekTySX12fYU2Wc7q0tX+mubp7XdgQMHsGbNGgBAWFgYVq9ejS+//PKZvg95ee+99+Dt7Y21a9cqvKyoqCiEhYXh4MGDfeI54dTP2uvr/ezmzZt4//33YWdnh6KiIsybNw8jRoyAq6srkpOT5R6rvK1atQorVqzA2rVr0dzc3PFMj49pZmdnMx6Px44fP66gwdVnc+TIEebu7s4aGxsZY4xVVlYyAwMD7jBAfHw88/b2ZgBYeHg49zkbGxvuAguRSMS8vb259wIDA9no0aNlypk+fTp78cUXub+dnJzYihUrGGOMlZSUMF9fXwaAvfnmm+zGjRvs6tWrTF1dnQUHB3OfOXjwIFu6dCn3FKPPP/+cAWDnz59njDH2xRdfsF9++YUx9vB8GTs7O2ZkZNStG9TPnDmT6enpsX/84x/s7Nmz7IcffmAaGhrMxMSE1dfXs3fffZcBYKmpqdxnmpubmZubW5fL6Gp9/f39mampqcxnx48fz9zd3RljD89DfOmllxgAtmbNGiYUClltbS1zc3NjVlZW7F//+he7efMmq6urY1OmTGFWVlbdqitjT2+3vqSiooLp6Oiwr7/+WiHLF4vFzMDAgH300UcKWX53UJ+V1Rt9tit16ay/MtZ527VNA8A+++yzbsWnSKmpqQyAwi+q8/f3ZwEBAQoto6uon8nqL/0sNjaWu1fqhg0b2MWLF9nx48eZnp4e09DQYMXFxXKLVVEKCwuZuro6O3ToUEdvtz8Hs+3JPH3lNkSMPXwUpbGxMTt69KjM9IULF8r8wzt37pxMJ2ppaWEAZM7LeXQZT+pE//nPf7i/ly9fzhwdHbm/33vvPQZA5qIJLy8vZm1tzRh7mEzp6Oiw3Nxc7v3y8nK2cOFCdvPmTVZUVMRGjhwp8wjNjz76iAFgv/32W5e/k5kzZzITExOZaR988AEDwL777jtWUFDA+Hw+CwkJ4d6PjIzs9onwndWXMcbmz5/friO5u7vLbLB27drFAMjcUuTjjz9mAGQeA/fhhx8yADI37u+srox13m59SUhICPP09FTIspOSkhgApd+Imvpse73RZzurC2Od99eutl1fTDAZe5g4tV14pAgNDQ1MIBC0+36UgfpZe/2lnzHG2CuvvML4fL7MnQeOHTvGALCPPvpIbttxRQoICJDZ8XjEznaHyHNycuDo6AhVVdVnGzdVgLi4OJSUlGD8+PEy0x8//KehoSHzt0AggL+/P9avX4833ngD1dXVWLp06VPLunjxIt577z00Njbi//7v/3D58mWIRCLu/bbvhc//7+mrpqamqKurAwDEx8dDKpXC0tKSe9/AwADHjx+Hra0tEhMT0draildffRWrV6/G6tWrUVxcjJCQkG6fjqCtrS3z9yuvvAIAEAqFMDU1RVBQEA4fPozKykoAQGhoKJYtW9atMjqrb3eXo6Ly31Wu7dCSQCDgppmbmwMAF3Obp9UV6Lzd+pKJEyfi9u3bCll2bm4u1NTUMG7cOIUsv6uoz3ZM0X22s7p0RVfbrq9ycHDA3bt3Fbb8goICtLa2wt7eXmFldBX1s471h34GPPzO+Hy+zDZwwYIFUFNTQ2Zmpty244rk4ODQ7jSINu0STD6fD7FYrPCguiMrKwuAbCLSVSdOnMCLL76I3bt3w8bGBrGxsU+dXyKR4IsvvsDLL7+McePGwc3NrVvlXb9+Ha2trWCMdfj+rVu3oKmpif3797d7zZ07t1tlPW706NFQU1NDY2MjAOCtt95CU1MT9u3bh5aWFlRWVsLKyqpHZcgTj8d74jSpVPrUzz5e1562W29qaWl5pnW5K/h8PiQSSaffn6JRn+0aeffZzurSFT1pu75ALBYrNPa2BKovbCepn3VNX+xnTyIQCGBiYsKtX319Oy4Wi2V2Kh7VLsG0s7ODUCh88kmbStC2N3bv3r1uf1YgEODo0aM4dOgQAMDf35/rlI+TSqWYNWsWMjMzERoaiqlTp3a7PG1tbTQ1NeHmzZvt3mtuboaGhgYKCwtRWFjY7v2Kiopul/coFRUV8Pl8ODg4AAAmT54MT09P7Nq1C5GRkU+80qs/erSu8mi33pScnKyw0Y9x48ZBIpEgIyNDIcvvKuqzXSPvPttZXbqiJ22nbIwxpKWlKXQE39TUFBoaGkhLS1NYGV1F/axr+mI/e5qWlhbY2NjIJVZFEwqFXKyPa5dgLl68GA0NDThw4IDCA+sqR0dHAA+vVnyUVCqFRCJ54ueam5uxe/duAMCKFSuQnJwMqVSKCxcuAHi40j16Ne/ly5dx7tw5zJgxg5vW3b0UFxcXAMCHH34oM4okFArx66+/Yvz48WCMYePGjTKfu3v3Ln744Ycul9ORvLw8tLa2YsmSJdy0t99+G8XFxfif//kfBAUF9Wj5T8Ln81FfXy/TFvX19QodRXu0rvJot97y999/49SpUwgODlbI8p2cnGBpaYmffvpJIcvvKuqzXdQ1570AACAASURBVCPvPttZXYDO+2tX264v9q/o6GgUFBRg/vz5CitDXV0ds2bNwo8//qiwMrqK+lnX9MV+9iTl5eUoLS3FokWL5BKrIt26dQsJCQlYsGBBxzN0dGbmhg0bmJ6eXp96rNS0adOYqqoq2717N2toaGCXL19mJiYmDAA7cuQIa2hoYMePH2cA2J49exhjjDU1NbHx48dzFyy1tLQwfX19lpiYyBhj7LXXXmMA2JUrV9ilS5fYhQsXGADm7e3NMjIy2M8//8zGjx/PtLS02LVr11hpaSlbu3ZtuxOZn3/+eaajo8Pd1T4gIIABYM8//zzbuXMne+edd9icOXNYa2srk0qlbPLkyQwAW7hwITt06BDbtWsXmzFjBquoqOjy9zF79mw2cuRI7ipqqVTK/vGPf7Q74V4sFjMzMzM2b968Z/reu1LfTz75hAFgW7ZsYbdv32Zbtmxh1tbWTFdXlwmFQsYYY9u2bWMAWHp6Orecr7/+mgFgFy9e5KZ98803DAD3ua7UNTk5udN26wvEYjGbMWMGs7W1Vejj5Hbv3s3U1dVZZmamwsroCuqzsnqrzz6tLox1rb92pe2EQiED0NnNlntNU1MTc3JyYrNmzVJ4WVeuXGE8Ho8dPHhQ4WV1hvqZrP7Uz0JCQhiPx5O5+HXdunVs5cqVco1VEaRSKfP392eOjo4yF2Y9ouMn+dTX1zMnJyc2fvx4mat5lam6upq98sorbOTIkczc3Jxt3ryZrVmzhr3yyissOjqanT9/nk2fPp0BYK6uriwqKoo1NTWxyZMns9n/H3t3HhdV9f8P/DUbOwOIyCKgoGJogIoKomguaLmk4b6mRVCiqbmghVuZiZ8yzS3NNE0tWSrDFXDJJQ1FxA0R0AQB2WRfZzm/P/zO/TkBCgrcAd7Px4MHcO+dmdfcuefe99zl3BEj2Pr165mvry/buXMn95xxcXHM2tqaOTg4sJCQEMbY04ZlaGjI3N3dWVRUFDt69Chr3bo1GzduHAsPD2d2dnYMAJs9ezbLyspi+/fvZ/r6+gwAW7VqFZPL5aykpIR99NFHrG3btszc3Jx99NFHLD8/n3vd3NxcNnXqVNamTRtmZmbGZsyYwdLS0uo0P+Li4tikSZPYm2++yXx9fdm8efPUuqB4lp+fH/f+6uL06dO1er8FBQVs1KhRzMDAgLm7u7MrV66w9957j82cOZMdPXqUnT59mjk7OzMAbOrUqSwpKYn99ddfrFu3bgwAGz58OLtx4wa7ePEi69GjBzddcnJyrd/r8z431YqGTwqFgvn5+TFdXV214rkhyGQy1rdvX9alSxde78JFbVZdY7RZxtgL38uL2itjL/7srl69yqZMmcIAMDs7O3bgwAG11+CDn58fMzQ0ZElJSY3yevPnz2eGhoZqX5r5QO1MXVNqZz4+PkwikbBZs2axcePGMR8fH7Z69epqC7ZXydoQVq1axSQSyfNuI17zrSIfPnzIOnTowLp06dJoDZY0jF69enF9pJHGV1payqZMmcK0tbXZ4cOHG+U1U1NTmY2NDevTp4/G3uqV1IzabO0plUq2aNEiJhaL2e+//95or1teXs4GDRrEzM3NWVxcXKO9Lqk/fLczHx8fpqOjU6tp+c76rKCgICYQCLg94jWo/laRwNPuYs6dO4e3334bPXv2xLZt217YjQF5ddbW1i88SXjfvn21vtXg6dOnMWjQIOjo6DTo65Dq3bx5EzNmzEBKSgqOHDmCIUOGNMrrWltbIzIyEkOHDkXfvn3r3H0GqT1qs/wpKiqCn58fQkNDsXfv3gY99/K/tLW1cfjwYbz99tvo168f9u7dW/O5aOSVteR2VlPWxlZWVob58+dj165d2LhxI/z8/J7/gBdVqmVlZczf358JhUI2ePBgFh8fX39lMGkQqjsETJgwgTk6Otbp/BVSPwoLC9mCBQuYWCxmffr0UeuQtzGlpaUxNzc3pqOjw7755puazpUhPKM2W3dnz55l7du3Z2ZmZiwyMpK3HBUVFczX15cBYNOmTWO5ubm8ZSHPp2ntzNvbmwmFQlZUVFRlnKZl/fvvv5mDgwMzNjZmv/32W20eUvMh8v+6fPky69GjB9PS0mJLly6lw24a7Pbt28ze3p7Z29uzv/76i+84LYpcLmc///wzs7KyYqampuyHH37gvaiTyWRszZo1TEtLi/Xv35/3u/yQqqjN1l5BQQFbsGABEwqFbMyYMSwzM5PvSIyxp3dYsbKyYpaWlrXdAJNGpkntbOnSpdw5qrNmzWLnz59XG68pWQsLC9miRYuYSCRib731FktNTa3tQ2tfYDL2dOO5ZcsW1qpVK2ZgYMDmz5/P/v3337onJqSZKS4uZps3b2b29vZMJBKxDz74QO1qSk0QGxvLXFxcmFgsZrNmzdKoXiIIeZGSkhIWFBTETE1NmYmJCdu7dy/fkap48uQJmz59OhMIBKx3797s2LFjfEci5KUUFRWxdevWMVNTU2ZsbMx27drF9QZQS3UrMFUKCwvZhg0bmK2tLROLxWzKlCksOjr6ZZ6KkCYtLS2NLV++nJmamjI9PT3m7++v0RfFKRQKtm/fPtahQwempaXFZs+ezR4+fMh3LEJqVFxczDZt2sQsLCyYgYEB++yzzzT+CNrVq1fZiBEjGADm4eHBTpw4UdeNMyG8KCgoYP/73/+YmZkZMzQ0ZJ999tnLnvbxcgWmikKhYH/++SfXd5WjoyNbuXIl170MIc1RaWkpCw4OZiNHjmRisZiZmZmxgIAAlp6ezne0WqusrGR79+5l9vb2TCgUsiFDhrDg4GCuXzxC+JaQkMACAgJYq1atmJaWFvP19WUZGRl8x6qTy5cvs5EjRzIArGPHjmzdunUa0/UfIc+6ffs2+/jjj5mBgQHT19dnH3/88av2If1qBeazLl26xPz9/Vnr1q2ZUChkAwcOZLt37+b9xFRC6kN5eTk7fvw4mz59OtPX12daWlps9OjRLCwsjJWXl/Md76VVVlay4OBgNnDgQCYQCJi9vT1bt25dnfueI6Q+lJSUsIMHD7IBAwZwRdn69eub/Hbkxo0bzN/fnxkZGTFtbW02depUdubMGfpCR3iVl5fHdu3axe0k7NKlC9u0aRPLy8urj6ffImCsfu/3pVAocObMGezbtw9hYWEoLy9H9+7dMXLkSIwaNQo9evSAQCCoz5ckpEFkZ2fj+PHjOHLkCE6ePInCwkK4urpi+vTpmDx5Mtq0acN3xHqVmJiIH3/8Ebt27UJeXh769OmD8ePHY9y4cWjbti3f8UgzVV5ejsjISISEhOCPP/5ASUkJBg0aBF9fX3h7e0MkEvEdsd6Ul5cjPDwcO3fuRFRUFExNTTF8+HCMHz8eb775JiQSCd8RSTOXl5eH8PBwhISEICIiAgKBAG+//TZ8fX0xePDg+qzPttZ7gfmsoqIiREZG4ujRozh69CgyMzNhY2ODESNGwMvLC56enjAzM2uolyekTsrKyvDPP//g9OnTOHbsGK5duwYdHR0MGjQII0eOxIgRI2BjY8N3zAZXXl6OiIgIhISE4M8//0RxcTE8PDwwduxYvPnmm3jttdf4jkiauOzsbERFReGPP/7A0aNHUV5ejgEDBmD8+PHw9vZudl/eqpOQkIDQ0FCEhobi+vXraN26NcaMGYORI0di4MCBkEqlfEckzURSUhIiIyPx+++/48yZMxCLxRg2bBjGjRuHUaNGwcjIqCFetmELzGcplUrExMTgyJEjOHbsGGJjY6FUKuHo6Ij+/fvD09MTAwYMoD0lpNEUFxfj4sWLOHfuHM6dO4crV66goqIC7du3x5tvvomRI0di0KBB0NXV5TsqbyoqKnDy5EmEhITg6NGjyMvLg62tLby8vDB06FAMHjwYpqamfMckGq6yshIXL15EREQEIiIicP36dYhEIvTv358rKlvyzoakpCSEhYUhNDQU165dg1AohJubG7y8vODl5YXevXtDLK7xviiEqMnLy8OpU6cQGRmJyMhIPHjwAAYGBhg2bBjGjh2LkSNHwtDQsKFjNF6B+V8FBQW4cOECzp07h/Pnz+Pq1auQyWTo0KED3N3d4erqCldXV3Tv3r0xZgRp5uRyOe7cuYOrV68iJiYGV65cQWxsLORyORwcHLgvOP3790e7du34jquRFAoFrly5goiICJw8eRLR0dFQKpVwdXVFv3794OHhAQ8PD1hZWfEdlfCsuLgY0dHRuHjxIi5duoRz586hpKQEnTp1wtChQzFs2DAMHDgQBgYGfEfVONnZ2VxxEBUVhZSUFEilUvTv3x99+vSBh4cHevXqBX19fb6jEg3x6NEjXLp0CZcuXcLFixcRExMDAOjZsye8vLwwZMgQeHh4NPYpGPwVmP9VUlKCS5cu4fz584iOjkZMTAyys7MhFArRuXNnruDs0aMHnJycYGJiwndkoqEqKioQHx+P2NhYxMTEICYmBnFxcSgrK4Ouri5cXFzQs2dP9OvXD/3794elpSXfkZukgoICnDp1CqdPn8aFCxdw69YtKBQKtGvXDn379kWfPn3g7u4OJycnaGtr8x2XNBClUonk5GRER0dzG7ibN29CoVDA1tYWHh4eeOONNzB06FDY2dnxHbfJSUhIQGRkJP766y9cunQJaWlpEIvFcHJygoeHB9zd3dG7d2907NgRQqGQ77ikgZWUlODGjRu4fPkyV1Q+evQIIpGIWyYGDRqEQYMG8V0naU6BWZ309HSuQFDtdcrMzAQAmJiYoEuXLujatSv3u2vXrlQstCCVlZVITEzEnTt3cPv2be53QkICFAoFJBIJOnXqxH05cXV1Ra9evajYaSAlJSWIjY3FxYsXceHCBVy6dAm5ubkQi8VwcHDg2qrqc7CwsOA7MqkjmUyGe/fucevkO3fuIDY2Vu1z7tevH/r27QtPT08qKBuAaruoamcxMTEoLy+HlpYWOnbsyK3runbtiu7du9MpLE1Yeno6t11TtTnV9k0qlaJ3797o27cvdxRJw3a8aXaBWZ2UlBRuhsfHx+PWrVuIj49HYWEhAKBNmzbo3Lkz7O3tYW9vjw4dOnB/m5ub85ye1FVRURHu37+P+/fvIzk5mfs7KSkJDx48gFKphJaWFhwcHNClSxe1n86dO9N5SzxSKpW4d+8e4uLicP36dVy/fh1xcXHIyMgAAFhbW8PR0REODg547bXX4ODgAAcHB9ja2tKeGJ7l5OQgISEBCQkJuHfvHu7du4f4+HgkJiZCoVDA0NAQzs7OcHFxQbdu3dCtWzc4OTlBR0eH7+gtTkVFBW7evIm4uDjExcXhxo0biIuLQ35+PgQCAezt7dG5c2d07twZDg4O6NSpExwcHGBtbU09umiAiooKJCUlce0sMTER9+7dw61bt5CXlwcAsLOzg4uLC1xcXLh216FDB56Tv1DTKzBrkpqaivj4eNy+fRtJSUlcIfLvv/+isrISAKCvr88Vnba2tmjbti2srKxga2sLS0tLWFtbt+gLOhqbXC5HZmYmUlJSkJGRgUePHiEtLQ2PHj3iisns7GwAgEAgQNu2bdW+ODg6OqJr167o2LEjFZJNSFZWFuLi4hAbG8sVMQkJCcjJyQEA6OjocMVm+/btYWtri/bt26Ndu3awtbWFsbExz++g6SsvL0dKSgpSUlLw8OFDpKSk4N9//+U2ck+ePAEA6OnpcZ9F586d4ezsjG7duqFDhw5UnGi4f//9Fzdu3MDNmzdx9+5drnhRFS16enpcsdm+fXvY2Nhw7c3W1lbT9oY1WQqFAo8fP8a///7LtbmUlBQkJSUhMTERDx8+hFKphEAggK2tLfeZdOnSBc7OznB2dm6oq7wbWvMpMGuiVCrVCpb79+/jwYMHSElJwaNHj5CRkcEVoADQqlUrrug0NzeHubk5zMzM0Lp1a+7H3NwcrVu3ppOsq1FZWYmcnBzk5OQgOzsbWVlZ3P85OTlIT09Heno6Hj16hMzMTCgUCgBPC0hzc3O0bdsW1tbWsLOzUysm7ezs6NB2M/fkyRPcu3ePKzgTExO5tqr6ogEAUqkU7dq1Q7t27WBjYwNzc3O0adMGlpaWaNOmDczNzWFpaQk9PT0e3w0/ZDIZsrKy8PjxYzx+/BjZ2dnIyMhAZmYm0tPTuY2bai8y8PSLd7t27dC+fXt07NiR29Pl4OAAGxsbKiSbmezsbK6dJSYmIjExkSt+nm1nBgYGXDuztraGhYUFzMzMYGFhwbU5CwuLplr8vDKFQsFt4x4/fozMzEy19qb64paWlgaZTAYAkEgkaNu2LWxsbNChQweunamKymZ2BKD5F5i18fjxY6SnpyMtLQ2pqanIyMhAamoqt9CoiqPy8nK1x+nq6nJFp7GxMaRSqdqPkZERTExM1Ibp6upCX18fWlpaVX7zhTGG/Pz8Kr/lcjkKCwuRn5+PgoICFBYWcr9VP6px+fn5yMzM5E5VUBGJRGrFuaWlJaysrGBjYwMrKyuusVlYWPA6D4hmKysr44rNZ/e6qTaKmZmZ3F43FX19fVhaWsLU1BQmJiYwNjaGsbEx9/d/f2tra0NfXx96enrQ1taGVCpt9E6+CwoKIJfLUVBQgMrKSpSUlKC4uBh5eXnIz8/nfj/7d15eHp48ecJ9mXuWnp4ezM3NYWFhAQsLC66QVO0NbteuHZ2jRzhlZWVcsZmamsq1tdTUVGRlZXEF1bN0dHRgZmaGNm3aVNvO/vujra0NY2NjiMViSKVSbhvY2PLy8qBQKFBYWIiKigqUlpaiqKiIa1//bW+qnydPnnDz4dnySVtbW60AV+0JVrWzdu3awdLSslndOOAFqMCsi+LiYrW9crm5uVzxmZ+fr1Z4qYox1XC5XP7C56+u4BQKhdV+QxSLxdV231ReXo6ysrIqw0tLS1FRUQGgakFZG9UV0FKpFMbGxjAyMoKxsTHatGmjVkyqfghpDBUVFcjOzq6y9y43N7faDYbq7+cRCAQwNjaGRCLhutSpqU0aGRlVOXe0sLCQ20uvUlZWxn1ZVW3YSkpK1I6kVEdfX7/GDXirVq1gZmYGKysrtGnThtujS90Akfoml8u57WBGRgZXeGZnZyMvL6/aoiw/P5/bi1cT1Rc7Q0ND7pQn1bBnVbftUyqVKCgoqPKcRUVF3LZXtQ2srk3+l76+fo2FsomJSZU9uebm5nRKQVVUYDaW0tJSFBQUoKysjFvQVRsV1e/i4mLIZDK1RqHaAP1XTYVkTYXnsxvIu3fv4tSpU1i6dCnXubGJiQm3MVVtQFXfMOmcN9KcqTaIqjb47IZIJpOp7U0EoPa3imoDxxjDsWPH4OrqCgsLi2o3kM/usVH9rZpO1e6MjIygra0NPT09GBoackUuIU1VSkoK+vbtiw4dOmDDhg3cETLVNq66ArC6YrCmbV91X/B0dXW5w86qv1UFrImJCbe91NHRga6uLrW1+rWVroxoJHp6ehpzTlhqaip++eUXiEQijB8/nu84hPDKxMSk3vY+KBQKiMVibNq0CWPHjq2X5ySkqVMoFPD19YVIJEJoaCgd2WohqC+QFsjGxgY+Pj748ssvucPmhBBCSENYunQp/vrrL4SFhVFx2YJQgdlCffbZZ8jJycEPP/zAdxRCCCHN1O+//45vvvkG27Ztg6urK99xSCOiArOFsrS0hJ+fH9auXVvt+SyEEELIq7h79y5mzpyJOXPmYNasWXzHIY2MCswWLCAgAAUFBdixYwffUQghhDQjRUVF8Pb2RteuXfH111/zHYfwgArMFszCwgKzZ89GUFBQtVeqE0IIIXXFGMPMmTORl5eH0NBQ6uO4haICs4ULCAhASUkJtm3bxncUQgghzcDq1asRHh6O4OBgWFlZ8R2H8IQKzBaudevW8Pf3R1BQEIqKiviOQwghpAmLiIjAmjVrsGnTJnh6evIdh/CICkyCRYsWobKyElu2bOE7CiGEkCYqKSkJEydOxJQpU/DRRx/xHYfwjApMAlNTU8ybNw/ffPNNlXuJE0IIIS9SUlKCd955B+3bt6cLRwkAKjDJ/1m4cCGUSiU2bdrEdxRCCCFNzPvvv4/09HT89ttv0NXV5TsO0QBUYBIAT+/jqtqLmZeXx3ccQgghTcTXX3+N0NBQHDx4EHZ2dnzHIRqCCkzCWbBgAUQiETZu3Mh3FEIIIU3AmTNnsGzZMqxbtw7Dhg3jOw7RIFRgEo5UKsUnn3yCjRs34smTJ3zHIYQQosFSU1MxceJEjBo1CgsXLuQ7DtEwVGASNfPmzYOOjg6++eYbvqMQQgjRUOXl5Rg7dixMTU3x008/QSAQ8B2JaBgqMIkaAwMDLFy4EJs2bUJWVhbfcQghhGigOXPm4O7du/jtt98glUr5jkM0EBWYpAp/f38YGBjQXkxCCCFVbN++Hbt378ZPP/0ER0dHvuMQDUUFJqlCX18fS5YswdatW5GZmcl3HEIIIRri8uXLWLBgAVasWAFvb2++4xANRgUmqdbs2bNhbGyMoKAgvqMQQgjRAJmZmRg3bhwGDBiA5cuX8x2HaDgqMEm1dHR0sGTJEmzfvh1paWl8xyGEEMIjmUyGiRMnQiKR4ODBgxCJRHxHIhqOCkxSIz8/P5iZmdFeTEIIaeEWLlyIq1evIjw8HKampnzHIU0AFZikRtra2li2bBl27tyJ1NRUvuMQQgjhwYEDB7Blyxb8+OOPeP311/mOQ5oIKjDJc/n4+MDKygpfffUV31EIIYQ0sri4OPj6+mLhwoWYOHEi33FIE0IFJnkuiUSCZcuWYdeuXXjw4AHfcQghhDSSvLw8eHt7w83NjXYykDqjApO80MyZM2FjY4O1a9e+0vMUFRXVU6Lmobr5kZWVhYMHD2LTpk24ffs2D6lIQ6N2oI7agWZSKpWYOnUqZDIZDh06BLFYrDaelmN1tBxXRQUmeSGJRILAwEDs3bsXycnJdX78jh07MGDAALUOeU+fPg13d3f8+++/9Zi0aahufgBAQkICfHx84OnpiTNnzqB79+4oLCzkKSWpb9QO1FE70GyBgYE4c+YMwsLCYGZmxg2n5VgdLcc1owKT1MqMGTNgb2+PNWvW1PmxPj4+UCqVUCgU3LC8vDykpqaipKSk1s+TkZFR59fWRNXNDwD49NNP8frrr8PGxgZ79+7FTz/9RLdga0aoHaijdqC5Dh8+jHXr1mHLli3o1auX2jhajtXRclwzKjBJrYhEIgQGBuLnn39GQkJCnR9rbW2tNmzs2LFIS0tD165da/Uc+fn5mDJlSp1eV1NVNz8A4OTJkzAyMgIAGBkZNZv3S56idqCO2oFmSkhIwLvvvgs/Pz+8//77VcbTcqyOluOaUYFJam3y5MlwcHB4qb2Yr6KkpAQTJkxo1odfysrK6vTtn7Q81A5IQysqKoK3tzccHR2xcePGBnkNWo5bDvGLJyHkKZFIhBUrVmDatGn49NNPq5xz8qzDhw/j6NGjMDExQVlZWZXDIdnZ2Th06BB69eoFNzc3AE+7w/j222/RpUsXZGRkoKKiAtu2bcPvv/+Ou3fvIi8vDx988AE6d+6MRYsWITMzE4GBgbC1tUVKSgpycnKwa9cumJqa4vr16zhw4ADCwsJw8+ZNzJs3D3/88Qfs7e3x66+/wt7ensty7NgxHDlyBBKJBNHR0XjvvffwwQcfAAAYY9ixYwfi4uJw7do1GBkZYevWrejUqVOd5t3z5sfevXsRFRUFAAgJCUFSUhI6duyIgICAOr0G0TzUDmo/P6gd8Isxhvfeew+5ubk4efIktLW1uXG0HKuj5biWGCF1oFAomLOzM5s4cWKN0xw4cIC5u7uzsrIyxhhjOTk5zMzMjFlYWDDGGLtw4QLz9PRkAFhoaCj3uM6dO7MLFy4wxhgrLS1lnp6e3LiRI0ey9u3bq73OG2+8oZbDxcWFTZs2jTHGWEZGBhsyZAgDwPz9/dnt27dZbGws09bWZpMmTeIes2/fPjZ58mSmUCgYY4x9+eWXDAA7deoUY4yxr776iv3000+MMcbkcjnr0qULs7CwYCUlJbWeZy+aH6phANiaNWtq/bxE88jlcm65pnagjtqBZluzZg0Ti8Xs7NmzasNpOVZHy3GtbaECk9RZSEgIEwgE7Pr161XGlZSUMEtLS3bw4EG14d7e3moNMCIiQm2FVFlZyQCwzZs3c9M8+xw1rZDWrl3L/T916lTm7OzM/b9s2TIGgOXk5HDD+vXrxzp16sQYYywrK4sZGRmx+/fvc+OzsrKYt7c3u3PnDktLS2Pm5ubcyooxxlasWMEAsF9//fUFc6lu84NWSM2DqsA8cOAAtYNnUDvQbJGRkUwkErHvvvtObTitz9XRclwnW+gQOamzsWPHwsXFBV988QVCQ0PVxp0/fx4ZGRlwcnJSG66lpaX2v56entr/EokEQ4cOxbx583Dnzh2sXbsWkydPfm6OM2fOAHh6vsuBAwcQHR0Nxhg3XiQSAYBa/23W1tZISkoCAFy4cAFKpRJ2dnbceDMzM4SFhQEAQkNDIZPJ4Ofnp/a6Pj4+0NXVfW42ldrOD9K8xMfHUzt4BrUDzfXw4UNMnjwZEydOxNy5c9XG0fpcHS3HdUMFJqkzgUCAVatW4Z133sH169fRrVs3btzdu3cBPF3B1NVvv/2GDz74ANu3b0dYWBhCQkLQv3//GqdXKBRYv349YmNjMWfOHLi5ueHy5cu1fr1bt25BJpOBMQaBQFBlfHx8PPT19fHDDz/U+b2ovMr8IE1XWloaAGoHKtQONFNZWRm8vb1hZWVV7edL63N1tBzXDV1FTl7K6NGj0atXL6xatUptuOqb3MOHD+v8nBKJBAcPHsTPP/8MABg6dCjXoP9LqVRi+PDhuHnzJoKDg5+74qqJVCpFeXk57ty5U2VcRUUF9PT08OjRIzx69KjK+Ozs7Fq9xqvMD9J0qfayUDt4itqBZpo9ezYePHiA3377rcpeFcieMAAAIABJREFUSIDW5/9Fy3HdUIFJXtqKFStw+PBhREdHc8OcnZ0BPL167lnVdUT7rIqKCmzfvh0AMG3aNFy+fBlKpRKnT58GAAiFQhQXF3PTR0dHIyIiAoMHD+aGqb691lbPnj0BAMuXL4dSqeSGx8TE4JdffoGTkxMYY1Wu/ktOTsa2bdtq9Rq1nR91yU00X7t27QBQO1ChdqB5Nm7ciH379uHAgQPo0KFDtdPQ+lwdLcd1xMOJn6QZcXd3Z8OHD1cbNmDAACYSidj27dtZSUkJi46OZlZWVtzFDyUlJSwsLIwBYN9//z1jjLHy8nLm5OTE5HI5Y+zpSeKtW7dmf//9N2OMsQ8//JABYFevXmVnz55lp0+fZgCYp6cnu3HjBtuzZw9zcnJiBgYGLC4ujj1+/JjNnTu3yknhAwcOZEZGRkypVDLGGHvrrbcYADZw4EC2ZcsWtnjxYjZq1Cgmk8mYUqlkvXr1YgCYt7c3+/nnn9nWrVvZ4MGDWXZ2dq3nUW3mR0xMDAPAPv3001f6PAi/nr2KnNqBOmoHmuPixYtMS0tL7aKamtByXPf5QcsxY4yuIiev6uTJkwwAu3TpEjcsPz+fzZo1i5mbmzNbW1u2atUq5uvry2bNmsWioqLYqVOn2BtvvMEAsN69e7PIyEhWXl7OevXqxUaMGMHWr1/PfH192c6dO7nnjIuLY9bW1szBwYGFhIQwxp6upAwNDZm7uzuLiopiR48eZa1bt2bjxo1j4eHhzM7OjgFgs2fPZllZWWz//v1MX1+fAWCrVq1icrmclZSUsI8++oi1bduWmZubs48++ojl5+dzr5ubm8umTp3K2rRpw8zMzNiMGTNYWlpanebRi+bH1atX2ZQpUxgAZmdnxw4cOKCWgTQdzxaY1A7UUTvQDOnp6czS0pKNHj2aK8yeh5bjus0PWo45WwSM0b5c8moGDBgAHR0dnDx5ku8ohPBKoVBALBYjNDQUY8eO5TsOIWpkMhkGDRqE7Oxs/PPPP9ytDAlpAFvpKnLyylauXInBgwfj3LlzL3VydlNlbW2NioqK506zb98+vPXWW42UiJDGR+2g6ZgzZw6uX79OxWU1aDmuf7QHk9SLgQMHQigU4tSpU3xHIYQ3tAeTaKqff/4Z7777LoKDgzFu3Di+45DmbytdRU7qxZo1a3D69Gmus1xCCCGaITY2Fn5+fvj000+puCSNhvZgknrj5eWFsrIyXLhwge8ohPCC9mASTZObm4uePXuiY8eOOHHiBHdHHEIaGO3BJPXn888/x8WLFxEZGcl3FEIIafEUCgWmTJkCxhh++eUXKi5Jo6ICk9SbPn364K233sLKlSv5jkIIIS3ekiVLcO7cOYSFhaF169Z8xyEtDBWYpF598cUXuHz5Mo4fP853FEIIabF+/fVXbNiwAdu3b4erqyvfcUgLRAUmqVeurq4YOXIkli9fTrfLIoQQHty8eRM+Pj6YN28eZs6cyXcc0kJRgUnq3erVq3Ht2jUcOXKE7yiEENKi5OXlwdvbGy4uLli/fj3fcUgLRgUmqXfdu3fHmDFjsHLlStqLSQghjUSpVGL69OkoKSlBSEgItLS0+I5EWjAqMEmD+PzzzxEXF4fff/+d7yiEENIirFq1ChEREQgODoaVlRXfcUgLRwUmaRCvv/46xo0bhxUrVkCpVPIdhxBCmrXw8HB8+eWX2Lx5M/r168d3HEKowCQNZ+XKlYiPj0doaCjfUQghpNlKTEzEjBkzMHXqVPj5+fEdhxAAdCcf0sCmTp2K69ev4+bNmxAKn36fuXHjBtasWYPPPvsMLi4uPCck5OV9/fXXyMnJ4f5njCE4OBh9+/aFtbW12rQLFiyAubl5Y0ckzVxxcTHc3d2hpaWFixcvQldXl+9IhADAViowSYNKTExEly5dsHfvXjg6OiIwMBDHjx8HYwxhYWHw9vbmOyIhL23OnDnYtm3bcy+mkMlkaNOmDdLS0rgvWYTU1bvvvovZs2fDzc2NG8YYw6RJk3Dq1ClcuXIFdnZ2PCYkRA3dKpI0rE6dOmH48OH45JNP4OrqisjISDDGoKWlhfv37/Mdj5BXMnnyZDDGUFFRUeOPWCzGtGnTqLgkL+3x48fYv38/PD09sWfPHm74+vXrERYWhoMHD1JxSTQOrfFIg4mPj8e0adNw5MgR5ObmgjEGmUwG4Ok3byowSVPn4eHxwqt1KysrMXny5EZKRJqj4OBgCIVCyGQyvPfee/jwww9x8uRJBAYGYv369Rg6dCjfEQmpggpMUu/u3buHKVOmoGvXrggJCYFSqYRcLlebRiaTISEhgaeEhNQPgUCA6dOnQyKR1DiNvb09evTo0YipSHOzb98+KBQK7v8ff/wRH374Iby9vfHJJ5/wmIyQmlGBSerdqVOn8Ouvv4IxhsrKyhqnS0xMbMRUhDSMyZMnc3vm/0tLSwvvvvtuIycizUlKSgquXbumdtMKuVyOtLQ0REZG4vTp0zymI6RmVGCSevfRRx9h//79EAqFEAgENU6Xnp6u9q2ckKbIxcUFDg4O1Y6rrKzEpEmTGjkRaU4OHjwIsVhcZbhMJkNhYSG8vLwQFBTEQzJCno8KTNIgpkyZgl9++QVCobDGixsUCgVSU1MbORkh9a+6w+QCgQDOzs41Fp+E1Mb+/furnGKkolAooFQqsXTpUrz77rsoLy9v5HSE1IwKTNJgJkyYgMOHD0MkEtVYZCYnJzdyKkLq35QpU6oUAWKxmA6Pk1dy9+5d3L59G8/rTVAikUBfXx99+vShe48TjUIFJmlQI0aMQEREBLS1tasUmWKxmK4kJ82Cvb09unfvrraMy+VyTJw4kcdUpKn75ZdfaryATLWsDRkyBPHx8fjwww+pKyyiUWhpJA3ujTfeQEREBHR0dNTOJRKJRFRgkmZjxowZ3AZeKBSib9++aNu2Lc+pSFN24MCBai8gE4vFaNWqFUJCQnDs2DHY2NjwkI6Q56MCkzSKfv364dy5c9DX14dIJALw9CT1pKQknpMRUj8mTpwIpVIJ4On5lzNmzOA5EWnKrl27VuUUIpFIBIFAgFmzZiE5ORnjxo3jKR0hL0YFJmk0rq6u+Pvvv2FiYgKxWAylUkl9YZJmw8LCAv3794dAIIBAIMDYsWP5jkSasF9//VXtnEqxWIxOnTrh77//xs6dOyGVSnlMR8iLUYFJGlWXLl1w7tw5tGrVCgDw4MEDnhMRUn+mTZsGxhiGDRvGLeOE1BVjDPv370dlZSUkEgm0tLSwevVq3LhxA+7u7nzHI6RWqnauRUgDc3R0xN9//43+/fsjPT0db775Juzt7bFgwQJ06tSJ73iEvJSSkhI8fPgQQqEQFRUVSExMpOWZ1FpJSQk2bdqES5cuQSKRICMjAwDg7u6OH3/8kZYl0uQI2PP6PyCkgcjlcvTu3RvXr18HYwwSiQQSiQRxcXHo2LEj3/EIqRO5XI5+/frh2rVrkMlktDyTOvnv8iMUCsEYw7p167B48eLn3rCCEA21lQ6RE15cvHgRsbGxXP9uMpkMcrkc27Zt4zkZIXV38eJF/PPPP9wVv7Q8k7r47/KjVCohFovx+PFjKi5Jk0UFJuHFo0ePqvTZJpPJkJKSwlMiQl4eLc/kVVS3/Mjlclp+SJNGBSbhRc+ePavcnUIsFtMJ7KTJSU5Oxv79+6tdnt3c3HhKRZoSWh+S5ogKTMKLzp07Y82aNWr3Kndzc8PcuXN5TkZI7eTl5WHhwoXo0qULHj58CF9fX7Xl2djYmPp5JbXSuXNnTJ06ldaHpFmhi3wIr+7du4fZs2dDLpfjzJkzdL4R0XgymQx79uxBYGAglEolFi9ejAULFkBLSwv37t3DlStXYGNjg7y8PIwbNw5LlizBl19+yXdsosHy8vLg4OCAUaNGYfDgwbCxsYGnpyetD0lTtpW6KSK8cnBwQPfu3am4JE1CeHg4PvnkE6SkpODDDz/E559/DiMjI268g4MDHBwcuP/37NmDd999F8bGxli8eDEfkUkT8Nlnn0EkEuHbb79VW54IacqowCS8s7GxQWpqKt8xCKnR1atXsXDhQpw/fx7jxo1DREQE7OzsXvi4adOmoaCgAHPnzoWxsTE++OCDRkhLmpLY2Fjs3LkTu3fvpuKSNCtUYBLe2djYIDs7G+Xl5dDR0eE7DiGc1NRUrFmzBrt27ULv3r1x4cIFeHh41Ok5/P39kZ2djY8++ghSqRQTJ05soLSkqWGMYf78+XBzc8P06dP5jkNIvaICk/DOxsYGjDGkpaWhQ4cOfMchBMXFxfj6668RFBQEGxsb/Prrrxg3btxLn8axatUqFBUVYfr06ZBKpXjrrbfqOTFpivbt24cLFy7g8uXLdIoQaXaowCS8s7GxAfB0bxEVmIRPcrkcu3fvxooVKyCTybBq1SrMnz8f2trar/zcX3/9NQoKCjBu3DicOHECnp6e9ZCYNFVFRUVYtmwZ/Pz80KtXL77jEFLvqJsiwrs2bdpAW1ubzsMkvIqKikL37t0xZ84cTJw4EcnJyQgICKiX4hIABAIBduzYgeHDh2PUqFGIjY2tl+clTdPKlStRUVGBzz//nO8ohDQIKjAJ7wQCAdq2bUsFJuHFtWvXMHDgQHh5eaF9+/aIj4/Hpk2bYGxsXO+vJRKJcODAAbi7u2PYsGFISEio99cgmu/OnTvYsmUL1q5di9atW/Mdh5AGQQUm0Qh0JTlpbGlpafDz80Pv3r1RWlqKc+fOITw8vMFP09DS0sJvv/2GTp06wcvLi24H2AJ98skncHJygo+PD99RCGkwVGASjWBtbU0FJmkUJSUlCAoKwmuvvYYTJ05g9+7duHz5cqOeE6mnp4c///wThoaG8PLyQlZWVqO9NuFXcHAwIiIisGXLFohEIr7jENJgqMAkGoH2YJKGplQqsW/fPnTs2BFBQUEIDAxEQkICZsyYwcsVvKampoiMjIRMJsOwYcOQn5/f6BlI4yotLcWSJUswc+ZM9OnTh+84hDQoKjCJRqACkzQk1QU8Pj4+ePvtt3H37l0EBATw3u+qlZUVIiMjkZmZCW9vb5SXl/OahzSsL7/8Enl5eXTrUNIiUIFJNILq3s3FxcV8RyHNyJ07dzBy5Eh4eXnB1tYWd+7cwY4dO9CmTRu+o3E6dOiAkydPIi4uDhMmTIBcLuc7EmkAycnJ2LBhAz7//HNYWlryHYeQBkcFJtEIqr4w09LSeE5CmoP09HT4+fnB2dkZmZmZOHv2LMLDw9GxY0e+o1XLyckJx44dw5kzZzBr1iwolUq+I5F6Nm/ePNjb22P27Nl8RyGkUVBH60QjPNvZeufOnXlOQ5qq0tJSbN68GV9++SWMjIywbds2+Pj4QCjU/O/Sbm5u+OOPPzBixAhIpVJs3bqV70iknoSHh+Po0aM4ffo0JBIJ33EIaRRUYBKNYGpqCj09PToPk7wUpVKJsLAwLFq0CE+ePMHChQuxdOlS3s+xrKvBgwfj119/xfjx42FmZoZVq1bxHYm8ooqKCixatAiTJk3CwIED+Y5DSKOhApNoDOqqiLyMU6dOYdGiRbh16xbee+89fP755zA3N+c71ksbM2YMfvzxR8ycOROGhoZYuHAh35HIK1i/fj0ePXqEiIgIvqMQ0qiowCQag64kJ3Vx9+5drFixAiEhIRgyZAhiY2Px+uuv8x2rXsyYMQMFBQWYN28ejI2N8f777/MdibyE1NRUBAUFYfny5WjXrh3fcQhpVFRgEo1BBSapjZycHHzxxRfYtm0bnJ2dcfr06WZ56HHu3Ll4/Pgx/Pz8IJVKMX78eL4jkTpasGABLC0tsWDBAr6jENLoqMAkGsPGxgbR0dF8xyAaqqysDN999x3Wrl0LQ0NDbN26Fe+//36zvhvKl19+idLSUkybNg1SqRTDhg3jOxKppVOnTiEsLAxHjx6FtrY233EIaXRUYBKNQXswSXUYYwgNDcWSJUuQnZ2NOXPmIDAwEAYGBnxHaxQbNmxAfn4+vL29ERERgb59+/IdibyAXC7H/PnzMXr0aAwfPpzvOITwQvP77iAtho2NDYqKilBQUMB3FKIhzpw5g549e2LSpEnw9PREUlIS1q1b12KKSwAQCATYuXMnBg0ahJEjR+L69et8RyIvsHHjRiQlJWHDhg18RyGEN1RgEo1hbW0NALQXk+DevXuYMGECBg0ahFatWiE2Nhb79u2DhYUF39F4IZFIEBwcDBcXF4wYMQL379/nOxKpQWZmJtasWYOAgADY29vzHYcQ3lCBSTTGs52tk5YpNzcXS5cuhZOTE27duoUjR44gMjISzs7OfEfjna6uLv78809YWlrCy8sLGRkZfEci1Vi0aBGMjIywePFivqMQwisqMInGMDIyglQqpQKzBaqsrMSmTZvQoUMH/Pzzz9i8eTNu3ryJESNG8B1No0ilUpw4cQLa2toYOnQonjx5wnck8oyLFy/iwIED2LhxI/T19fmOQwiv6CIfolFq29l6UVERDA0N1YZlZWUhKioK2dnZGDJkCLp27dpQMUk9UV3As3TpUjx+/Bhz587FZ599VuWzBegzV2ndujUiIyPRr18/vPXWWzh16lSDnJNK87tuFAoF5syZg8GDB+Odd96pcTqar6SloD2YRKPY2Njg0aNHNY7fsWMHBgwYAEdHR7XhCQkJ8PHxgaenJ86cOYPu3bujsLAQBw8eRM+ePSGVSuHm5oZjx4419FsgtfTPP//A09MTkyZNgqurK+7cuYN169ZV2fjW9TMHgPz8fAQGBmLZsmWN9n4aU9u2bREZGYmUlBSMHj0a5eXl9fbcNL9fzvbt23Hnzh1s3ry52vG07iItDiNEg/j4+LDBgwfXOF4ul7N+/foxCwsLteHe3t5s2bJljDHG8vPz2YEDB9iGDRvY8OHD2caNG9n8+fOZvr4+EwgELDIyskHfA3m+e/fusfHjxzOBQMAGDx7MYmNjnzt9XT5zxhg7dOgQGz9+PAPA5syZ0zBvQkPExcUxExMTNnr0aCaTyerlOWl+111OTg4zNTVlS5YsqXEaWneRFmYLFZhEo6xevZo5ODg8d5pJkyZVWUnr6+uzdevWcf8XFRVVKVQvX77MhEIhGzp0aP0FJrWWm5vLAgICmLa2NnvttddYcHBwrR9bm8/8WQUFBS2m4Ll06RLT19dn06dPZwqFol6ek+Z33fj4+DALCwtWUFDw3Olo3UVakC10DibRKC86RF6dsrIylJSUqA37559/8NVXX6kNc3NzQ48ePZCUlPTKOUntyWQy7NmzB4GBgWCMISgoCP7+/hCLX371U91n/qyWdOcUd3d3/P777xg1ahSMjIxqPET7Kmh+1ywmJga7d+/Gzz//DKlUWqfH0rqLNGdUYBKNYmNjg9LSUuTm5sLU1BQAcPjwYRw9ehQmJiYoKytT655l7969iIqKAgCEhIQgKSkJHTt2REBAQLXPL5VK67wRIC8vPDwcCxYsQHp6Oj7++GN8+umntZr/9fmZtwReXl44ePAgJkyYAHNzcwQGBtbp8TS/X45SqcScOXPg4eGByZMnVxlP6y7SovG9D5WQZ929e5cB4M7LO3DgAHN3d2dlZWWMsafnOpmZmakdZsrJyWEA2Jo1a5773HK5nJmZmbHdu3c33BsgjDHGoqOjmaenJxMIBGz8+PHswYMHtX5sfXzm5eXlLfKQ7Z49e5hAIGAbNmyo9WNofr+8Xbt2MZFIxK5fv15lHK27SAu3ha4iJxrl2c7WS0tLsWjRInz88cfQ0dEBAJiamsLT0/OlnvvPP/9Et27dMHPmzPqKS/4jNTUVM2bMgJubG+RyOS5evIjg4GC0b9++Vo+v78+8pZk5cybWrl2LhQsXYs+ePS+cnub3yyssLERgYCD8/f3h4uKiNo7WXYTQIXKiYfT09NCqVSukpqbi/PnzyMjIgJOTk9o0WlpadX7evLw8rFmzBseOHYNAIKivuOT/5OfnY926ddi0aRNsbW1x6NAhjB8/vs7PU5+feUu1dOlS5Ofn44MPPoBUKsXYsWNrnJbm98sLDAyEUqnE6tWrq4yjdRchVGASDWRjY4PU1FTIZDIAT+/D/KoWLFiAb7/9Fubm5q/8XOT/U13As3z5csjlcqxatQoLFix46QLl7t27AOrnM2/JvvrqK+Tl5WHatGkwNTXFG2+8Ue10NL9fzu3bt7F9+3bs2LEDxsbGVcbX53yldRdpqugQOdE4qgJTVaQ8fPjwlZ5v69atGDNmDPr3718f8cj/iYqKQvfu3TF37lxMmjQJycnJCAgIeKW9X/X1mbd0AoEA27dvx5gxYzB69GhcvXq12ulofr+cOXPmwMXFpcZD1rTuIoQKTKKBVLeLdHZ2BvD0CstnKZVKKBQK7n/GWI3PdfDgQejq6mLMmDFqw1VXb5K6u3r1Kt544w0MHToUXbp0QXx8PDZt2lTtnpy6qo/PnDwlFAqxb98+7paSd+7cqTINze+6O3jwIM6dO4etW7dCKKx+E0rrLkKowCQaSLUHs2/fvhgwYAD27NmD77//HqWlpbhy5QouXLiA7OxsHDx4EKWlpUhJSQHw9MT6Zx07dgybN2+GTCbDjh07sGPHDnz//ffw9/fnDmGR2nv06BH8/Pzg5uaG8vJynD9/HsHBwbC3t6+313jVz1xFNfzZjXlLJJFIEBoaCkdHRwwdOhT//vuv2nia33VTXFyMJUuW4P3334ebm1uN09G6ixBQN0VE8+zbt49paWkxhULB8vPz2axZs5i5uTmztbVlq1atYr6+vmzWrFksKiqKXb16lU2ZMoUBYHZ2duzAgQMsPz+fRUdHM11dXQagyo+2tjbLzc3l+202GUVFRWzlypVMV1eXtWvXju3du5cplcoGe72X/cxVjh8/ziZPnsyN37lzJ0tPT2+wvE1Bfn4+69GjB+vYsSPLyMioMo7md+0sWbKEmZiYsKysrBdOS+su0sJtETBGxz6IZjl79iwGDhyIjIwMWFhY8B2nxZLL5di9ezdWrFiByspKBAQEYP78+S36ri1NWXZ2Nvr37w+JRIKzZ8+iVatWfEdqUhITE+Hk5IRvvvkG/v7+fMchRNNtpUPkROOo+sKs6y0jSf2JiopCjx49MGfOHIwePRoJCQkICAig4rIJMzMzQ0REBAoLCzFixAgUFxfzHalJmTdvHjp16gQ/Pz++oxDSJFCBSTSOtbU1BAIBUlNT+Y7S4ty+fRsjRoyAl5cX2rVrh/j4eOzYsQNmZmZ8RyP1wMbGBpGRkXjw4AHeeecdVFRU8B2pSfjtt99w4sQJbNmyBWIx9e5HSG1QgUk0jra2NszMzKjAbERpaWnw8/ODi4sLsrOzce7cOYSHh6NDhw58RyP1rFOnTjh58iSuXr2KyZMnQy6X8x1Jo5WVlWHRokWYOnUqBgwYwHccQpoMKjCJRlJdSU4aVklJCYKCguDo6Ijjx49j9+7d+Oeff+hWgc2ci4sLjh49ioiICPj4+FA3RM8RFBSEnJwcBAUF8R2FkCaF9vUTjUQFZsNSKpXYv38/li5diqKiIixcuBBLly7l7ptMmj8PDw/88ssvGDt2LIyNjbFx40a+I2mclJQU/O9//8Pq1athZWXFdxxCmhTag0k0EhWYDUd1AY+Pjw9GjRqF5ORkrFq1iorLFmjUqFH46aefsHnzZqxbt47vOBrn448/Rtu2bTF37ly+oxDS5NAeTKKRbGxs8Mcff/Ado1mJj4/HkiVLcOTIEQwZMgSxsbHo2rUr37EIz6ZMmYL8/HzMmTMHJiYmdJX0/4mMjMThw4dx/Phx6j2BkJdABSbRSDY2NkhPT4dcLqerNl9RTk4OvvjiC2zduhXdunXD2bNn6WIFomb27NnIzc3F7NmzYWRkhEmTJvEdiVeVlZWYO3cuxo0bhzfffJPvOIQ0SbTlJhrJxsYGCoUCjx8/hrW1Nd9xmqTS0lJs3rwZa9euhVQqxbZt2+Dj41Pj/ZNJy7Z8+XIUFhZixowZMDQ0xIgRI/iOxJtvvvkGKSkpOHHiBN9RCGmyqMAkGklVVKamplKBWUdKpRJhYWFYvHgxcnJysGjRIgQEBEBXV5fvaETDrV+/Hvn5+Rg/fjxOnDiB/v378x2p0aWlpWHt2rVYtmwZ2rdvz3ccQpos2pVBNJKVlRVEIhFd6FNHp0+fRs+ePTFlyhQMGzaMu4CHiktSGwKBAN9//z1GjhyJUaNGISYmhu9IjW7RokVo06YNFi9ezHcUQpo0KjCJRpJIJDA3N6cCs5YSEhIwYcIEDB48GKamprh27Rp27NgBc3NzvqORJkYkEmH//v3w8PDAW2+9hbt37/IdqdFcuHABhw4dwsaNG6lXBUJeERWYRGNRV0Uvlpubi3nz5uH111/H7du3cfToUURGRsLJyYnvaKQJ09LSQlhYGDp37gwvLy88fPiQ70j1qrq7F8nlcvj7+8PLywujRo3iIRUhzQsVmEQjFRcXw8jICDExMdi9ezdWrVqF999/H15eXti+fTvf8XhXWVmJTZs2oUOHDggNDcXWrVtx48YNDB8+nO9opJnQ09PDkSNH0Lp1a3h5eSEzM7Pa6SoqKprUnYAYY+jevTsOHjyolnvLli24e/cuNm/ezGM6QpoPAWtKawbSbJ09exZBQUFITk5GRkYGiouLuXEikQhisRhyuRwKhQLh4eEYOXIkj2n5wxhDaGgoAgICkJWVhTlz5uCzzz6DoaEh39FIM5WVlYX+/ftDW1sbZ8+ehYmJCTeuuLgYY8aMQUBAALy8vHhMWXvJycno2LEjAKBPnz74/vvvYWFhgc6dO8Pf3x9r1qzhOSEhzcJWKjCJRsjLy4O1tTVKS0ufO51AIEB2djZMTU0bKZnmuHz5MhYuXIjLly9j6tSpCAoKgqWlJd+xSAuQkpICT09PtG3bFpGRkdDX18eTJ08wdOhQxMTEYNiwYU2mS59Dhw4XHljsAAAgAElEQVRh8uTJYIxBLBZDoVDAw8MDKSkpiI+Ph76+Pt8RCWkOttIhcqIRTExM8PHHH0MikTx3Ont7+xZXXN67dw8TJkxAnz59oKenh2vXrmHfvn1UXJJGY2tri2PHjuHevXt455138PDhQ3h4eODGjRsAgIiIiCZzMVBMTAy3npHL5WCMITo6Gvn5+di1axcUCgXPCQlpHqjAJBpj4cKFEIlENY6XSCQYOHBgIyZqOD/88MMLz1t78uQJli5dCmdnZ9y6dQvh4eGIjIyEi4tLI6Uk5P/r2rUrjh49ivv376Nfv364f/8+ZDIZgKdtc9OmTTwnrJ3Lly+jsrJSbZhMJkNRUREWLFgAV1dXXLp0iad0hDQfVGASjdG6dWv4+vrWuBdTqVTCw8OjkVPVv2+++Qa+vr44dOhQteOfvYDnxx9/RFBQEG7evNlizzslmsPQ0BBFRUXIzMzkikvg6TK7Z88e5Obm8pjuxRhjiI2Nfe74W7duoW/fvtiwYUMjJiOk+aECk2iU53VurDpXqik7dOgQFi9eDIFAgEWLFqGiooIbxxhDSEgIHB0d8emnn8LPzw/JycmYN2/ec/fsEtIYrly5gr59+yIvL0+tuFRRKBTYtWsXD8lqLzk5We0CwpoMGzYMH3zwQSMkIqT5ogKTaBRra2u8++671e7FlEqlcHBw4CFV/fjrr78wffp0AE+LycePH+O7774DAERHR6N///6YNGkSXF1dcefOHaxbtw5SqZTPyIQAAKKiojBgwAAUFxdXW1wCT89n3LhxY43jNcGVK1cgFD5/s/fee+8hPDycemYg5BVRgUk0zrJly6qcaC8UCtGvXz8IBAKeUr2a27dvY9SoUVAoFNy5lwqFAqtXr8bbb78NNzc3aGtrIyYmBsHBwWjXrh3PiQl5qrS0FEuXLkVZWdkLL4DJzMxEWFhYIyWru5iYGIjF4irDhUIhRCIRtmzZgp07d1Y7DSGkbqjAJBrH3t4ekyZNUtuLKRaL4enpyWOql5eeng4vLy+UlZVBqVSqjausrERaWhqOHDmCqKgodOvWjaeUhFRPT08P0dHRCA4Ohq2tLYRCYY1f9AQCAdatW9fICWuvugt8xGIx9PX1ERERAX9/f56SEdL8UIFJNFJgYKDa7dwqKyvRt29fHhO9nMLCQnh5eSEnJ6fa29PJZDLExcXBzs6Oh3SE1I5QKMT48eORmJiI7du3w8zMrNq9fEqlEnFxcRp5FTZjDNevX1cbJpFI0LZtW0RHR2PQoEE8JSOkeaICk2gkR0dHjBkzhtuLKRKJ4OrqynOquqmsrMTo0aORmJj43PPShEIhli5d2ojJCHk5EokEvr6+ePDgAb7++muYmJhUOV9aIpFo5BXY9+7dQ0lJCfe/WCyGq6srrly5gtdee43HZIQ0T1RgEo21fPlybq+fk5MT9PT0eE5Ue0qlEtOnT8eFCxdeeNGDTCZDeHg4/vrrr0ZKR8ir0dPTw7x585CSkoIvvvgCenp63B5NmUyG33//HSkpKTynVBcTE8Nd4CMQCDBjxgycO3cOZmZmPCcjpHmiApNorO7du2Po0KEA0OQ6WA8ICEBoaGi1h8WfJRQKoa2tDQBYtGjRCztfJ0STGBgYICAgAA8ePIC/vz8kEgl3+8WtW7fyHU/NtWvXoFQqIRQKsWHDBvz4448vvHMYIeTl0b3ISa1VVFSgpKQE+fn5KCoqQnFxMYqLi1FYWIjCwkIUFxejvLwcpaWlqKioQEVFBUpLS6FQKFBYWAgAyM/PB2MMRUVFXPElk8lq7JuupKQEmZmZ0NfXh5aWlto4oVAIIyOjah8nEAhgbGzM/a+npwdtbW1oa2tDT08PIpGI6wLIxMQEwNNOpMViMaRSKQwMDKCvrw+pVAojIyMYGBhwP6rpa/Ldd99h3rx5VYZLJBIoFApuI2dtbY0ePXrA2dkZTk5OeP311+Hg4PDCblQI0QQymQyFhYUoKChAQUEBCgsLcf/+ffz00084f/48tLW1sWTJElRUVEAul3NtvrrfwNNzJPPz86t9LdU65b/+286fpWrzAKCjo4Pbt2+jqKgIffr0gZ2dHXR1daGjowNdXV1uvaCvrw9DQ0NIpVIYGhrCxMSE+1sqlTapoyiE8GwrFZgtjFKpRG5uLp48eVLl97N/5+TkIDc3F4WFhcjLy3tu/3fA0+LMwMBAbaUtFothaGiothGQSqUQiURVCsbnFW179uzBe++9V2VDoipgq1NZWal2vpUqf1lZGcrLy7miVqlUoqCgAABQUFAApVKJ/Px8lJSUVLtBU9HX14eBgQGkUilMTU3RqlUrmJqaIjc3F8ePH1fbE9mqVSs4OjqiZ8+e6N69O15//XV06dIFurq6NT4/IY2psLAQmZn/r707D2vi2vsA/g1hk32HsClYkUUWtZUooFbQCmpbxVoV3G5Fq7Xt67V9ve1bq9beantbb9/7qqV6r60KVqvULlZvFVyxFRUsqyyKyBYIIEsIWyDn/cMnc424YA2ZAL/P8+QJczKZ+U1CzvzmzJkz1aitreV++7W1tZBKpVyZqryhoQFNTU1oa2u777IMDAxgamoKhUIBc3NzuLi4wMDAAGZmZlydoDrAu/cgUVU/3MvQ0BCmpqbdyu/9nd9N9XsG7iSoCQkJGDduHExMTNDW1obW1lauDlEtp6WlBU1NTQ+sV1RxW1tbw9raGvb29rCzs4OtrS3s7Oxgb2/Pld39oINGMgBRgtlfyOVyVFRUQCqVQiKRQCKRQCqVqpVVVVVBKpV2G8tu0KBBXJKkSpRUD0tLS1hbW3MtemZmZrCysuJa80xNTR/YgqApOTk5GDFiRK+u435USWhDQwOam5shl8vR3NyM+vp67u/GxkYuMS8pKUFmZiaAO4NOt7S0dBuWaNCgQRCJRHBycoKjoyNcXFxgb28PZ2dnrszZ2RkODg50+o48sdbWVlRUVEAikaC8vBxVVVUoKytDVVUVysvLIZFIUFFRgdbWVrX3mZiYwM7ODg4ODrC3t+cSKDs7O1hZWcHCwoJr2bO0tISVlRXXymdsbMwtRyqVwsHBQdub3Y1cLodcLu9xLKqzLvX19ZDJZGhqauKeVeW3b99GTU0Nl4zX1taipqYG9fX1asvS19eHo6MjXF1d4eTkBDc3N4hEIri6ukIkEsHFxQUuLi4PPBtDSB9FCWZfIZVKcevWLZSWluLWrVtqj9LSUty+fZubVyAQwMHBAQ4ODnBxcYGDgwNEIhFEIhEcHBzg5OSk1upGLWm9p7GxkWv5qaurg1QqRWVlJaqqqlBdXY3KykruQODubgJCoRAikQiDBw/GkCFD4O7ujsGDB3PPQ4YModN1BIwxVFRUoLi4GDdv3kRxcbHa3xKJhJtXKBR2S3RUz46OjlwyaWdnR3XCE+js7FRr8VX93h+V2FtZWcHT0xMeHh7dnocMGdKtixAhOo4STF1SXl6OoqIiFBYWcs/Xr19HSUkJVxEJBAKIRKJuSYe7uzvXCubg4EB3ouiDWlpauJbmioqK+x5MqE7nA4CdnR08PDwwbNgweHl5wcvLi/ubbjHZvzQ3NyM/Px95eXm4du0a8vPzce3aNZSUlHBdOYyNjeHh4dEtQVG1kDk6OtI97XVMfX09KisrUV5ejpKSkm4HCqrWUD09Pbi4uMDLyws+Pj7w9fWFt7c3fH194ejoyPNWEHJflGBqW2dnJwoKCpCVlYXc3Fy1hFLVl8jS0hLDhg3jHh4eHlwi6ebmRkeyA1hjYyNKS0tRUlKCW7du4ebNmygsLERhYSFu3rzJ9ZN1dHTE8OHDuYTT398fAQEBcHFx4XkLyMMoFArk5OQgIyMDOTk5yMvLQ0FBAW7dugUAMDIywvDhw+Ht7Q1vb28MHTqUSyadnZ377K1Uyf01NDRwyWZxcTEKCwu5gwzVBVGqPt6qxHPkyJEYOXIknXInfKMEszdJpVJkZWUhMzMT2dnZyMrKQl5eHtrb22FgYAAvLy8uCbi7FYqOSMkf0dnZiZKSEi7hVB24FBQUoKysDMCdnVFQUBCXcAYEBMDPz49OifKgvb0dWVlZyMjIQEZGBtLT05GdnY2Ojg6YmprC19cXvr6+8PHx4VqrPD09qRWSAAAkEgnXmp2bm4v8/Hzk5ORAKpVCIBBg6NChGD16NEaNGsU9bGxs+A6bDByUYGqKTCbD5cuXcfHiRaSlpeHy5ctc/yeRSAR/f38EBgZyQ9L4+vrSRRxEa+rr65GVlYWsrCxkZ2cjMzMTubm5kMvlEAqF8Pb2xpgxYyAWiyEWi+Hn50eJjIZVV1fjwoULOH/+PFJTU5GZmcldaT1y5Ei1RMDb25s+f/KHlJeXcwctqkdFRQUAwNPTEyEhIQgLC0NoaCi8vb2p1Zv0Fkow/6jc3Fz89ttvXEJ57do1dHV1wc3NDcHBwRCLxQgMDERgYCDdKYLoJKVSiRs3biAzMxMZGRm4ePEiLl++jObmZpiZmeHpp5/m/pdDQkLo//gxlZSU4OzZszh37hwuXLiAgoICCIVC+Pv7IywsDGKxGKNHj8awYcNoGBvSq6qqqpCRkYFLly4hNTUVFy9ehFwuh729PUJCQjB+/HiEhoZi9OjR9L9INIUSzJ6qqqrC+fPnkZycjOPHj6OsrAympqYICgrC6NGjMXr0aISFhcHDw4PvUAn5w7q6upCfn4/09HSkp6fjwoULuHr1KpRKJTw9PREREYGIiAhMmTKF+njdo6urC7/99huOHj2K5ORkpKenw8DAAAEBAQgJCUFoaCjCw8PpNCXhnep3fuHCBSQnJ+PMmTOoqamBra0tJk2ahIiICDz//PNwcnLiO1TSd1GC+SCtra04ceIETpw4gZSUFBQUFMDY2BghISGIiIhAeHg4Ro4cSVdrk36vqakJZ8+eRUpKCpKTk5GbmwtDQ0OMHTsW4eHhiIqKwujRo/kOkxd1dXX47rvvcPz4cSQnJ0Mmk8HX1xdRUVGIjIxEaGgoXZRH+oScnBwcP34cx44dw4ULF6BUKhEcHIyoqCjMnDkTvr6+fIdI+hZKMO/W3NyMY8eOISkpCT///DNaW1sxatQohIeHIyIiAiEhIXQxBBnwJBIJl2wmJyejoqICHh4emDVrFqKjoyEWi/t1v66Wlhb88MMP2L9/P3755RcYGBjg2WefRVRUFKKiojBkyBC+QyTkiTQ2NuLkyZNcwllVVYWgoCDExsZi7ty5NBoF6QlKMNva2vDdd9/h0KFD+OWXX6BQKDBhwgRER0dj5syZdIqAkEe4cuUKkpKSkJSUhKKiIri6umLWrFmYN28exGIx3+FpTEpKCvbs2YMjR46gra0NkydPxvz58/Hiiy/CzMyM7/AI6RVKpRLnzp1DYmIikpKS0NjYiAkTJmDBggWYO3cuNbqQBxm4Ceb169fx5Zdf4quvvoJMJkNERARmzZqFF198Eba2tnyHR0iflJ2djaSkJBw+fBi5ubkYOXIkXn31VcyfP79PJmEdHR04cOAAtm7diszMTIjFYsyfPx8vv/yyTtwCkRBtam9vx7Fjx5CYmIijR4/CwsICK1aswGuvvUa/B3KvgZVgMsbw008/Yfv27UhOToarqyuWLVuGpUuX0tiThGhYWloavvjiCxw8eBBGRkZYsGABXnvtNXh7e/Md2iM1NTVhx44d+L//+z/U1NTgpZdewpo1azBq1Ci+QyNEJ1RXV2PHjh344osvIJPJEBsbi7feegvDhw/nOzSiGwZOgpmSkoK1a9fi6tWrmDJlClasWIFp06bRWHOE9LK6ujp8/fXXiI+PR3FxMebMmYP3338fPj4+fIfWDWMM+/btw9q1ayGXy7FkyRKsWbMG7u7ufIdGiE5qb2/HwYMH8fHHH6OwsBArV67EBx98QKNMkP6fYObm5mLjxo04dOgQIiIi8Le//Q1BQUF8h6U1UqkUycnJqKmpQUREBPz8/PgO6ZFkMhnMzc3VyvridmjD/T4rXcUYw9GjR7F+/XpkZmYiJiYGn332mc6Mr5mRkYHXX38dFy9e1LnYelNf/G1RHaF7lEolEhIS8Pbbb6Orqwvr1q3D66+/TuNqDlzbwfqp1tZW9uc//5np6ekxsVjMzpw5w3dIvSYlJYUFBwezmzdvqpXn5+ezGTNmsNLSUvbCCy8wAwMD1tjYyE+QPRAfH8/Gjx/PXFxc1Mr72nZow7Zt21hoaCjz9fXlO5TH1tXVxXbv3s0cHR2Zg4MD+/HHH3mNp7Ozk61bt47p6emxCRMmsMzMTF7j6Q1URxBtuX37Nlu1ahXT19dnkydPZlKplO+QCD+29csEs7S0lAUGBjILCwv21VdfMaVSyXdIverw4cPM2dmZ5eTkqJXPmjWLvfPOO4wxxhoaGlhiYiIf4fVYZ2cnCw0NZU5OTmrlT7IdlZWVGo1RVygUCubv78+8vb35DuUPa2hoYIsWLWIA2J///GfW1dWl9RjkcjmbOnUqMzY2ZvHx8Vpfv7ZQHfFg/bWO4Nvly5eZp6cnc3V1Zbm5uXyHQ7Sv/yWYBQUFzNnZmY0YMYIVFxfzHQ6vTE1N2ZYtW/gO47HMnTu3287jj25HfX09mzhxoqZC0zlTp07t0wmmSmJiIjM2NmazZ89mCoVCa+tta2tjEyZMYHZ2duzSpUtaW68uoTqif9cRfKuvr2dhYWHMzs6u28EN6fe29avOEVKpFM899xzc3d2Rmpo6oG/b2NraCrlczncYT+yPbodcLsecOXNQUlKi+aCIRs2fPx8nTpzAsWPH8Nprr2ltvcuXL0dmZibOnDmDZ555Rmvr1RVUR1Ad0dusrKzw73//G97e3njhhRfQ0NDAd0hEi/pVgvnGG29AIBDg6NGjOncF27lz52Bvbw+BQID33nuPK09JSYGFhQXWr18Pxhji4+OxYsUKBAcHY8qUKSgqKgIAVFRUYMuWLRgxYgRu376N5557DoMHD0ZdXR1qamqwbds2pKWlAQD27NmDZcuWAQAOHTqEuLg4fPzxx/jhhx9gbm4OgUCAzz//HB0dHQCA3377DSKRCB999NFjbdOxY8ewcuVKvPnmmxg7dix27doFADh48CDMzc3h5uYG4M5dITZt2gShUIixY8eqLeOHH37AsmXLsHbtWrzxxhuQSCTcaw/ajp44cuQI8vPzUVtbi7i4OHz66afca4cPH8brr7+Ot956C5GRkXjvvffQ3t7e4+3+/fff8fbbb8PT0xNyuRxLly6FnZ0dxowZg+Li4h5/Brm5uXj33XcxfPhwlJeXY8OGDXB3d4efnx9Onz6NtrY2rF69GkOHDoW7uzt++eWX+8aTkpKCKVOmwMbGBs899xwXA3BnKJG4uDhs2rQJcXFxmDlzJurq6nq8rdoSFhaGxMRE7Ny5Ez/99FOvr+/kyZPYs2cPEhISdOZiEKoj+k8doYntz8vLw//8z//A19cXFRUVeOGFF2BjY4MxY8bg4sWLGv+ueouJiQm+++47yOVyvP/++3yHQ7SJ7zZUTcnNzWUA2E8//cR3KA/06aefMgDsu+++48oUCgULCwtjSqWSbd68mX399deMsTt9jXx9fZmTkxOTy+Xs+PHjzNvbmwmFQrZ+/Xq2c+dONmbMGPbtt9+ysLAwBoAdPnyYW25tbS0DwD788EO1GP7yl78wAOzy5ctcWXt7OwsODn6sbdm7dy+bN28e12/ur3/9KwPAUlJSGGOMTZkyhbm6uqq9x9/fn4nFYm46MTGRicVi1traysVsb2+vdvrrQdvRE9OnT2dDhgxRK9u6dSsLCQlhHR0d3PKHDRvGxo8f3+O+uhKJhEVERDAA7LXXXmO5ubns6tWrzMjIiM2dO5eb71GfgVQqZQsWLGAA2LJly1h6ejprampiwcHBzNPTk61atYrl5eUxmUzGxo0bxzw9PdWWNXXqVGZra8v+9Kc/sX//+99sx44dzMTEhDk7O7Pm5mbGGGMTJ05kL7/8MveewMBAFhsb28NPUPtefvllNmrUqF5fT3h4OJs2bVqvr+dxUR3RP+oIxp58+8+dO8d8fX2ZUChk//Vf/8VOnz7NkpKSmK2tLTMxMWGVlZUa+660YefOnczQ0JDV1dXxHQrRjv7TB/ODDz5g7u7uOn1BT3NzM7OxsWHR0dFc2dGjR9n27dtZRUUFc3R0VLvQ4f3332cA2IEDBxhjjL3yyisMALt+/brack+cONHjnUdZWRnT19dnS5cuVYth06ZNPd4OqVTKLC0t1fq4SqVSNmvWLJaXl8cYY+zFF1/sVnmKxWKu8pTL5UwkErH9+/erzTNr1qxe23lUV1czU1NTtm/fPrX5vvrqKwaA7d27t8fLfueddxgAVltby5WFhoayYcOGcdOP+gwYY2z79u0MAMvKyuLK1q9fzwCwq1evcmXr1q1jANSuyJw6dSpzdnZWW/57773HALD//d//ZYzdSTA/+ugj7vWYmBgWEBDQ4+3UtrNnzzIAvdp/WiaTMaFQyL799tteW8cfRXVE/6gjNLH9jDG2ZMkSpq+vzyW7jDF28OBBBoC9//77GvmutEUmkzEjIyP2zTff8B0K0Y7+0wfz5s2b8PPzg0Ag4DuUBzI1NcXChQvx448/ora2FsCdUyXz5s3Dr7/+CoVCgeXLlyMuLg5xcXGorKzE0qVLuXu9GhgYQF9fH0OHDlVbromJSY9jcHV1xUsvvYSEhAQuhm+//Rbz58/v8TJSU1OhVCrV+rja29sjKSmpx4Nnnz9/HhKJBP7+/mrlhoaGPY7jcV28eBFyuZw7LaUyffp0AMCZM2d6vCzVAP36+vpcmaurK2Qy2WPFpFrO3WPFubq6ArjzfauoBvpWfWcqFhYWatNLliwBAKSnpwMATp8+jXfeeQetra345z//iUuXLqGlpeWxYtSmESNGAABu3LjRa+soLy9HV1eXTt5xhOqI/+jLdYQmth+4Uz/o6+ur1QUzZ86EoaEhsrOzNfJdaYuZmRlcXV2pz+sA0m8STEtLS9TX1/MdxiMtW7YMCoUCCQkJaGhogFAohLW1Na5duwZTU1Ps2rWr2+P555/XaAyrV69GW1sbdu7ciY6ODtTW1sLT07PH78/JyYFCoQB7gjH68/PzAagnUb3t1q1bAIDbt2+rldvZ2cHExASVlZVai+Vh7neQpCpTKpUPfe+QIUNgaGiI1tZWAEBXVxc2b96MRYsWwcvLC8HBwZoPWINUFwFYWVn12jpUSXlTU1OvreNJUB1xR1+uIzSx/Q9iYGAAZ2dndHZ2Anjy70qbGhsbux0Uk/6r3ySYY8eOxZUrV9Q6gOsiHx8fhIWFYffu3Th48CBiYmIA3GlhKC8vR3l5ebf31NTUaDSGZ555BiEhIdi+fTuOHj2KGTNmPNb7LSws0NbWhry8vG6v9bQjvKoVQlWha4OqNeHui2DupostWo9LT08P+vr6GDFiBJRKJaKiopCdnY1vv/0W48eP5zu8R/rhhx9gaWnZqxfeiEQiuLm54cSJE722jidBdcQdfbmO0MT2P0xHRwcXy5N+V9qSnp6O2tpanT/IJZrTbxLMF154AY6Ojnj33Xf5DuWRli1bhuzsbOzduxeTJk0CAPj7+4MxhrVr16rNe+PGDezYseOx1/GoI+e33noLlZWVWLNmDV566aXHWvbTTz8NAFi3bp1ai1p6ejq++eYbAHdOHTc3N6Orq4t7vbm5mZs/ICAAwJ0rP++mVCrV3vMkLQB6enpobm7mpsViMczNzfH999+rzVdRUYGWlhaNtwI96jPoDSUlJVAoFJgzZw4uXbqEEydOIDw8nHu9t1pVNKGurg5btmzB4sWLuVO+vUEgEGDx4sXYsWOHTl5RD1AdAfTtOkIT2/8gUqkUVVVViI6O5sqe5LvSlo0bNyIwMBCjR4/mOxSiJf0mwTQyMsL27duxZ88exMfH8x3OQ82ePRvW1taYPHky1/du8uTJeOaZZ7B//35ER0cjISEBO3bswPLly7mxAVWV0b1jiVVXVwNQ759XWloKAA/sbzdjxgy4ubkhMDAQtra2jxV/SEgIIiMjceTIEURERGD79u347//+b2zcuBGxsbEA7uwMGxoasHnzZhQWFuLDDz9Ee3s7CgsLkZGRgZCQEEyYMAFfffUV4uPj0dLSgsuXLyM1NRU1NTXYv38/WlpaHrkdD+Ps7Iza2lqkp6fj7NmzMDExwebNm3HhwgWkpKRw8/3jH//AggULuB15TzQ2NgIAd5oKuPM9tLa2cju8R30GwH9O0969HNWy726VUvXtvLv1QygUor6+nhsDkDGGTZs2Yf369fD29uZOq+/ZswfZ2dn4+uuvkZeXh+rqamRlZXH/N7qgvb0dc+bMgZGRETZu3Njr63v77bdhbGyMxYsXq+3gdQXVEX27jtDE9qu0t7cjOzubm/7rX/+K2NhYiMViruxJvittiI+Px9GjR/H555/zHQrRJn4uLuo9H374IRMKhewf//gH36E81Lp165hEIlErq6urYzExMczBwYHZ29uzhQsXsoqKCsYYY7t27WL29vYMAFu0aBF3hfHp06fZxIkTGQA2ZswYdvLkSZaRkcHmz5/PADAPDw+WmJjIGhoausWwfPlydujQoT8Uv1wuZytWrGAuLi7M0dGRrVixQm0djY2NbMaMGczMzIyJxWJ2+fJl9qc//YktXryY/fzzz4yxO7d0W7JkCXN0dGTu7u5sw4YNbNmyZWzJkiUsOTmZXblypUfb8SCZmZnM1dWVeXl5qW3nkSNH2JQpU9iqVavY+++/zz799NPHGn3g1KlTzMPDgwFgK1euZFKplCUkJDBTU1MGgG3YsIF1dnY+8jM4deoUCwgIYABYTEwMu379Ojt79iwLCgpiAFhUVBTLyspiFy5cYKNGjeLmu3HjBrd9c+fOZVOnTmXLli1jb775ptpVwowx9uqrrzJzc3MmFotZcnIy+/nnn5mdnR2bPXs2N5QR31K4y1kAABkeSURBVBobG9lzzz3HLC0ttXof8IsXLzJjY2O2ePFi1tnZqbX19hTVEX23jtDU9i9dupQZGBiwJUuWsNmzZ7OlS5eyjRs33ve2qk/yXfWmxMREZmBgwDZu3Mh3KES7+s8wRXfbsmULEwgEbMmSJaypqYnvcHTWM888w40vRwgf0tPTmY+PD3N2dmZXrlzR+vqPHz/OTExM2LRp02h8vvugOoJfS5cuZcbGxj2aV9e+q66uLvbhhx8yPT09tmbNGp0eQpD0im36923W7OPWrl0LX19fvPLKKzhz5gw++eQTREdH6/QQRtp26tQpTJo0CcbGxmrlrq6uj+yEvnfvXkRGRvZmeA/VmzH2he3vD+RyObZs2YJPPvkEISEhOHHiBDc8kzZNnToVKSkpmDNnDkaNGoVt27ZxQ9IMdFRHaH/Zf9SDviu+FBUVYeXKlTh37hw+//xzvP7663yHRPjAd4rbm6qqqlhsbCzT09NjwcHB7MyZM3yHxCvVnSHmzJnDfHx8WE1NDd8hkQFGoVCw3bt3M1dXV2Zpack+//zz+57u07aamho2b948JhAIWFRUFCssLOQ7JF5QHaFbZs2axfT09JhMJuv2mi5+V83Nzewvf/kLMzQ0ZAEBASwtLY3vkAh/+s9A6/fj6OiIffv2IT09HZaWlpg4cSKmTZuG06dP6+yVtL3J1tYWbW1tuHLlCuLj42FnZ8d3SGSAaG5uxs6dO+Hj44Ply5cjKioKBQUFePPNN9UGmeeLnZ0d9u/fj9OnT6O8vBz+/v54++23dWZsVG2hOkJ3vPPOO/jll1+gVCrxxhtvIDU1Ve11Xfqu2tvb8c9//hPe3t748ssv8dlnnyE9PR1jxozhLSbCPwEbQJlWcnIy1q9fj19//RXe3t5YsWIFFi5c2KuDOhMykOXm5uKLL77Avn370NHRgZiYGLz77rs6OxA0cOeK/i+++AIfffQRbt++jblz52L16tUICgriOzRCdEpNTQ2++OIL7NixAw0NDVi8eDE2bdoEe3t7vkMj/Ns+oBJMld9//x3x8fFITEyEUqnEvHnzEBMTg/Hjx3O37iOE/DH19fX48ccfsXv3bpw7dw7Dhg3D8uXLsXjxYp0cQuVB2tvbsX//fmzduhU5OTkIDw/HihUrMG3aNJ3p60YIH9LS0rB7927s27cPJiYmePXVV7Fq1So4OTnxHRrRHQMzwVRpampCQkICdu3ahd9//x329vZ48cUXER0djUmTJmn1FmWE9GU1NTX4/vvvkZSUhFOnTkFPTw/Tp0/Hq6++ivDw8D59gR1jDCdPnsTf//53nDhxAhYWFpg1axZiY2MxYcIEnTjFT0hvKywsxP79+5GYmIjr16/Dx8cHr7/+OhYtWvRY97onA8bATjDvVlRUhKSkJCQlJeHKlSuwtrbG888/j6ioKEyaNIn6IhFyj7y8PJw8eRI//vgjzp49C0NDQ0RGRiI6OhrTp0/vl/ccrqysxIEDB5CQkICrV6/CxcUFc+fOxYwZMxASEgJ9/X45MAcZoPLz8/Hzzz/j4MGDuHz5MkQiEV5++WXExsbSHXnIo1CCeT8lJSVISkrCkSNHkJaWBqVSicDAQISHhyMiIgJhYWF0xEYGnIqKCiQnJyMlJQXJycmQSCSwsrLClClTMHv2bERFRcHU1JTvMLXm2rVr2L9/Pw4ePIiioiJYWVlh8uTJiIyMRGRkJJ0uJH1Oa2srTp8+jWPHjuH48eMoLi6GjY0NZsyYgZiYGEyaNIm6kZGeogTzURobG3H27Flux5qXlwcjIyOMHTsWoaGhCA4OhlgsphZO0q8wxlBQUIC0tDRcvHgRZ86cQX5+PoyNjTFu3DjuYGv06NG0w8GdMyDHjx/HsWPHcPbsWbS3t2PUqFEIDw9HWFgYQkJCYG1tzXeYhKjp6OjAlStXkJqaijNnzuDMmTNoa2tDUFAQIiMjERUVBbFYTL9x8kdQgvm4KisrkZKSgpSUFFy8eBGFhYVgjOGpp56CWCxGcHAwgoODERQURH04SZ9RX1/PJZNpaWlIS0tDfX09jI2NMWrUKISGhiIiIgKhoaEYNGgQ3+HqtJaWFpw6dQrHjx/HmTNncO3aNQgEAvj5+SEsLAyhoaEICwvjZWB5MrDJZDL89ttvSE1Nxblz53Dp0iW0trbCyckJ48ePx3PPPYfIyEiIRCK+QyV9HyWYT+r27dvcDlm1g25oaICxsTH8/PwQEBAAf39/BAQEIDAwkFo6Ca+USiVu3LiBrKwsZGVlITs7G1lZWSguLgZjDEOHDlU7UBo5ciQdKD2huro6pKam4vz580hNTUV6ejo6Ozvh5uaGUaNGqT2cnZ35Dpf0EzKZDL///jsyMjKQkZGBq1ev4tq1a+js7MRTTz2F0NBQjB8/HqGhoRg2bBjf4ZL+hxJMTVOdWrx06RK3E8/KykJ1dTUAQCQSwd/fH4GBgfD19YW3tzeGDRvWp4ZvIbpPqVTi1q1bKCoqQn5+PnJycpCZmYnc3FzI5XIIhUI89dRTCAgIQEBAAIKCghAcHEzj12mBXC7nDkhVO//i4mIAgJOTE5dsBgQEwMfHB15eXjA0NOQ5aqKrGGO4desW8vPzkZ2djatXryIjIwNFRUVQKpWwtbXl/qeefvpphISEUAsl0QZKMLVFKpUiKysLmZmZyM7ORnZ2NvLy8tDW1gYAsLGxgZeXF/cYNmwY9zyQLpwgj6e6uhoFBQUoKipCYWEh93z9+nXufsl2dnZcS7rq4MbPz49OdeuQhoYGLtlUPa5fv46uri7o6+vD09MTfn5+8Pb2ho+PD3x9fTF8+HCYmZnxHTrRks7OTty4cQN5eXnIz89HXl4erl27hvz8fMjlcgCAs7MzgoKC1FrFBw8ezHPkZICiBJNv9fX1yM3NRV5eHoqLi1FcXIzc3FwUFhais7MTAGBtbQ2RSARnZ2d4enpyD1WZh4dHnx5nkDxYfX09939RXFyMyspKSCQSFBcXo6ioCE1NTQAAIyMjuLi4wNfXF35+ftz/yIgRI+hq5j5KoVCgrKyMqx9Uz9euXUNLSwuAO3XD3XXC3Q93d3caNqmPuff3fvejtLSU2yeIRCLud676zfv7+8PR0ZHnLSCEQwmmruro6OCSiJKSEpSWlqK0tBS3bt1CaWkpJBIJN++gQYPg4eEBR0dHODs7w8HBAS4uLnB0dIRIJIKTkxOcnJzoNLyO6OzshFQqhUQigUQiQVVVFSorKyGVSlFRUQGpVIqysjJUVlaiq6sLAGBoaAg3NzcMHjwY7u7uGDx4MAYPHowhQ4bAy8sLLi4uPG8V0ZbOzk7cvHkT165dQ3FxMW7evKn23NraCgAwMDCAu7s7XFxc4ObmBpFIBFdXVzg7O8PZ2Rmurq4QiUR0+l0LGGOorq6GRCJBRUUFKioqUFlZifLyckgkEpSVleHWrVuQyWQAAH19fbi7u8PDw4N7eHp6wsvLC8OHD6ezWqQvoASzr2pvb79v0imRSNQSlY6ODu49RkZGcHBwgEgkgo2NDWxsbGBra/vIv8mDdXR04Pbt26irq8Pt27e5h2q6rq6O+7u2thZSqRRSqRR3/+zMzMzg4uICBwcHODs7w8nJCc7OzmrJpEgkolZq0iNVVVVqSefdCU1lZSWqqqrU/v8cHR3h6OgIBwcH2Nvbw87ODra2trCzs4OdnR0cHBzUyighvZMw1tbWoq6uTu1ZKpWitrZWrUz1mSsUCu791tbWakm+m5sb3N3d4enpCQ8PD7i5uVHrM+nrKMHs72pqargj56qqKlRXV6O6uvqBCdHdCamKhYUFzMzMYGpqCgsLC1haWnLT5ubmsLKygpmZGVdmYGAAMzMzCAQCWFlZAQAsLS2hp6cHU1NTGBoawtjYWK0PoImJCYyMjDS23TKZjDudBNzp48YYQ3NzMxQKBVpbW9HW1gaFQoHm5mYwxrh5Ghoa0NzczD0aGxshk8m46aamJjQ1NaG5uZnrQ3s3MzMztSTdzs6Om1Yl+KpWZWdnZxq0n2iVQqFAdXU1ysrKuBa1qqqq+yZHdXV1UCqVau83MTGBubk5LCwsYGFhAWtra7Vp1d/W1tYQCoWwsLCAnp4eLC0t1eoE1bigVlZW3MGTkZGRRn4Pqt+1iup3r3qWy+Xo6OhAS0sL2tvbufpA9fuWyWRoampCY2MjGhoauGnVa42Njd3WaWFhwSXodyfkIpGISyJVrcjU/5kMAJRgEnXNzc1qrW/19fVcMtXc3AyZTKaWgMnlctTX13PTqgpb1UdME+7eAd2tt9Zzd8JsZmbWbdrS0pJLui0sLLq1+lILD+kv7tdS19jYyCVbTU1NaGhoUEu+7i5XKpVoaGh44jgMDQ3ve1q4o6ODu8DlSagOcFW/aVWibGlpCSsrK7XEWXWQrUogqWWXkPuiBJP0nq6uLu4ilPr6egD/aVlUtRioNDU1cf0N79bZ2cn1S7p69Sr27duHrVu3AgDXKnI/gwYNgrGxMTdtYWEBoVDI7UhUOyxVCwshpPfce4bg3mcVVcvivVQHrkqlEmvWrMGSJUsQEBDw0Drg7jsnqc6c3Pus6TMnhBAOJZik7zhw4ABiYmLum4gSQvo/1bBNhw8fRnR0NN/hEEIebLse3xEQQgghhJD+hRJMQgghhBCiUZRgEkIIIYQQjaIEkxBCCCGEaBQlmIQQQgghRKMowSSEEEIIIRpFCSYhhBBCCNEoSjAJIYQQQohGUYJJCCGEEEI0ihJMQgghhBCiUZRgEkIIIYQQjaIEkxBCCCGEaBQlmIQQQgghRKMowSSEEEIIIRpFCSYhhBBCCNEoSjAJIYQQQohGUYJJCCGEEEI0ihJMQgghhBCiUZRgEkIIIYQQjaIEkxBCCCGEaBQlmIQQQgghRKMowSSEEEIIIRpFCSYhhBBCCNEofb4DIGQgkclkMDc379G8qampSEtLg4uLC2bPno2CggKkpKTA3NwcUVFRcHR07OVoCSG9geoBMhBQCyYhWvDll19iwoQJ8PHx4cpOnToFsViMkpKSbvOvXr0aJSUlCAoKwsKFCyEWi7Fz505Mnz4dGzZswKxZs7QYPSFEE6geIAMJtWASogVLly5FQkICurq6uLL6+nqUlZVBLperzZuZmYlt27ZBLpfD0NAQ33//PaZNm4atW7fC09MTx48fh1Qq1fYmEEKeENUDZCChFkxCtEAoFMLV1VWtLDo6GhUVFfDz81MrP378OIyNjWFoaAgAGD58OADA0tISAODr64uJEyf2ftCEEI2ieoAMJJRgEqJjampq1KYLCwt5ioQQwheqB0hfRwkm6XPeffddDB8+HOXl5diwYQPc3d3h5+eH06dPo62tDatXr8bQoUPh7u6OX375Re291dXViIuLw6ZNmxAXF4eZM2eirq4OwJ1TUhMnToRAIEB4eDiqqqrw97//HcbGxti8eTMUCsVjxfnDDz9g2bJlWLt2Ld544w1IJBK112tqarBt2zakpaUBAOrq6hAXF4djx46htbUVcXFxiIuLwyeffAIAWL9+PeLi4vDzzz//0Y+OkH6hrKyM6gGqB4iuY4T0Ed988w3T09NjCxYsYADYsmXLWHp6OmtqamLBwcHM09OTrVq1iuXl5TGZTMbGjRvHPD091ZYxceJE9vLLL3PTgYGBLDY2lpuuq6tjIpGI+fv7M8YYW7t2LUtISHjsWBMTE5lYLGatra2MMcZqa2uZvb09c3JyYowxlpqaysLCwhgAdvjwYbX3vvrqq8zY2JibTkhIYADYlStXHjsOQvqTzs5OBoDt3r2b6gFCdNs2asEkfY5YLAYArFq1CqNGjYK5uTmmTp2K4uJivPLKK/Dx8YGZmRnCw8NRXFzc7VRTYGAg9/eIESOQlZXFTdvY2OCzzz5DdnY2Nm7ciKKiIsTExDxWfC0tLXjrrbfwxhtvwNjYGABga2uLsLAwbp6QkBCsW7fusbedEAJYWFhQPUCIjqOryEmfIxQKAQB6ev85PlJ1nDcwMODK3N3dAQC1tbWwt7cHAJw+fRoA0NraisTERFy6dAmMMbXlz5s3D7t27cKGDRvUdjo9df78eUgkEvj7+6uVqzrrq5iYmDz2sgkhd1A9QIhuoxZM0i8IBIIHlimVSq6sq6sLmzdvxqJFi+Dl5YXg4OD7Lm/x4sUAgH/961+PHUt+fj4A9Z0cIaT3UT1AiO6gBJMMGEqlElFRUcjOzsa3336L8ePH33c+uVyO/fv3IyYmBtu2bUNmZuZjrUfVQnHr1q0njpkQollUDxCiHZRgkgHj0qVLOHHiBMLDw7kyhULR7dTYunXrsGbNGmzduhXm5uZYuXJlt3keJiAgAABw6NAhtXKlUqk2wPKDMMbU1vc46yaEPBzVA4RoByWYpM9pamoCAHR2dnJljY2NANTHjpPJZACA9vZ2AP85VbZnzx5kZ2fj66+/Rl5eHqqrq5GVlYXq6mqkpaWhrKwMkydPhoODAzZt2oRff/0VX375ZY/jCwkJwYQJE/DVV18hPj4eLS0tuHz5MlJTU1FTU4P9+/ejpaUF1dXVAO70DbtbaWkp2tvbufjLy8sB3LlogBByB9UDhOg24YYNGzbwHQQhPZGTk4OkpCRUV1ejuroaMpkMgYGByMnJwccff4yqqirU1NQgICAAN27cwJYtWyCRSCCXyxEUFAR/f39UV1fj5MmTSEtLw8yZM/Hss8/i6NGjKC0thZ2dHRYtWoRx48ZhypQpEAgEKCoqwnfffYeTJ0/CxsYGY8aM6VGsM2fORFVVFXbt2oX4+HiYmZlBJBIhICAAY8eORUVFBf72t7+hpKQEUqkUHh4ecHJywjfffIOdO3eivb0dhoaGaGhowPbt21FVVYWqqipYWlrCy8urlz9pQnQTYwwffPABfH19cfDgQaoHCNFdlwWM2t1JH3HgwAHExMT06PQSIaT/6erqgr6+Pg4fPozo6Gi+wyGEPNh2GqaIkMfg6urKnWp7kL179yIyMlJLERFCtI3qAUIejRJMQh6Dqh8UIWTgonqAkEeji3wIIYQQQohGUYJJCCGEEEI0ihJMQgghhBCiUZRgEkIIIYQQjaIEkxBCCCGEaBQlmIQQQgghRKMowSSEEEIIIRpFCSYhhBBCCNEoSjAJIYQQQohGUYJJCCGEEEI0ihJMQgghhBCiUQLGGOM7CELuVV5ejkWLFqGrq4srk8vlkEql8PDwUJvX19cXO3bs0HaIhJBetnz5chQUFKiV3bx5E46OjjAxMeHK9PX1sW/fPohEIm2HSAi5v+36fEdAyP24urqipKQExcXF3V4rLS1Vm3722We1FRYhRIscHR2xc+fObuX31gFeXl6UXBKiY+gUOdFZCxcuhIGBwSPnmzdvnhaiIYRo2/z58x85j4GBARYvXtz7wRBCHgslmERnxcTEQKFQPPB1gUCAwMBAeHl5aTEqQoi2eHt7w8/PDwKB4IHzdHZ2Yu7cuVqMihDSE5RgEp311FNPITAw8IE7F6FQiEWLFmk5KkKINi1cuBBCofC+rwkEAjz99NPd+mUTQvhHCSbRaQ/buXR1dWHOnDlajogQok3z5s1Tu9jvbkKhEAsXLtRyRISQnqAEk+i0uXPnQqlUdivX09NDaGgoXFxceIiKEKItbm5uCA4Ohp5e992VUqnESy+9xENUhJBHoQST6DRnZ2eMGzeu285FIBBQywUhA8SCBQu6dZURCoWYOHEiHB0deYqKEPIwlGASnbdgwYJuZQKBADNnzuQhGkKItj2oK8z96gZCiG6gBJPovJdeekmtH6ZQKMTUqVNha2vLY1SEEG2xs7NDeHi4Wj2gp6dHB5mE6DBKMInOs7a2xuTJk7mdi1KpRGxsLM9REUK0KTY2Fqobz+nr62PatGmwtLTkOSpCyINQgkn6hNjYWO5iHyMjI0yfPp3niAgh2vTiiy9yN17o6uqig0xCdBwlmKRPeP7552FkZATgzo7G1NSU54gIIdpkbm6OGTNmAAAGDRqEadOm8RwRIeRhKMEkfYKpqSnX34paLggZmGJiYgAAs2fPhrGxMc/REEIehhJM0iccOXIEFy9ehJ6eHnbt2oWbN2/yHRIhRIuuX7+Of/3rXxAIBLhw4QJ++uknvkMihDyEgKl6TROio06dOoWIiAgIBAIolUoYGBjA1dUVubm5GDRoEN/hEUJ6WXNzM3x9fVFVVQWFQsGNi3v27FmEhobyHB0h5D62Uwsm0Xl79+6Fnp4ed5GPQqHAzZs38euvv/IcGSFEG86fP4/y8nIoFAoAd0aS0NPTQ0JCAs+REUIehBJMovNkMhnubWgXCASQyWQ8RUQI0abm5uZud/JhjKGpqYmniAghj0IJJtF5U6dOVdu5CAQCDBo0COPGjeMxKkKItoSEhMDQ0FCtjDGGyMhIniIihDwKJZhE5y1duhQrV67k+l3Z2Njg0KFDcHBw4DkyQog2ODs748CBA7C2tgZw525eq1evphElCNFhdJEP6TMaGxtRVVUFT09PbsBlQsjAoVAoUFxcDJFIBAsLC77DIYQ82HZKMAkhhBBCiCbRVeSEEEIIIUSzKMEkhBBCCCEapQ+gmO8gCCGEEEJIv1H3/y/spRWZClONAAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1376,7 +1372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1385,27 +1381,27 @@
      "text": [
       "HEAD df_w_cudf:\n",
       "          x         y  distance_cudf\n",
-      "0  0.868484  0.758179       1.152866\n",
-      "1  0.318385  0.046299       0.321734\n",
-      "2  0.844744  0.442833       0.953778\n",
-      "3  0.436758  0.348251       0.558602\n",
-      "4  0.197671  0.520553       0.556820\n",
+      "0  0.952256  0.706716       1.185849\n",
+      "1  0.647146  0.038779       0.648307\n",
+      "2  0.872885  0.094287       0.877962\n",
+      "3  0.525044  0.096420       0.533824\n",
+      "4  0.319330  0.745514       0.811026\n",
       "\n",
       "HEAD df_w_numba:\n",
       "          x         y  distance_numba\n",
-      "0  0.868484  0.758179        1.152866\n",
-      "1  0.318385  0.046299        0.321734\n",
-      "2  0.844744  0.442833        0.953778\n",
-      "3  0.436758  0.348251        0.558602\n",
-      "4  0.197671  0.520553        0.556820\n",
+      "0  0.952256  0.706716        1.185849\n",
+      "1  0.647146  0.038779        0.648307\n",
+      "2  0.872885  0.094287        0.877962\n",
+      "3  0.525044  0.096420        0.533824\n",
+      "4  0.319330  0.745514        0.811026\n",
       "\n",
       "HEAD df_w_cupy:\n",
       "          x         y  distance_cupy\n",
-      "0  0.868484  0.758179       1.152866\n",
-      "1  0.318385  0.046299       0.321734\n",
-      "2  0.844744  0.442833       0.953778\n",
-      "3  0.436758  0.348251       0.558602\n",
-      "4  0.197671  0.520553       0.556820\n",
+      "0  0.952256  0.706716       1.185849\n",
+      "1  0.647146  0.038779       0.648307\n",
+      "2  0.872885  0.094287       0.877962\n",
+      "3  0.525044  0.096420       0.533824\n",
+      "4  0.319330  0.745514       0.811026\n",
       "\n",
       "Max Difference cudf to numba: 2.220446049250313e-16\n",
       "Max Difference cudf to cupy: 2.220446049250313e-16\n"
@@ -1440,7 +1436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1450,7 +1446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1459,27 +1455,27 @@
      "text": [
       "HEAD df_w_cudf:\n",
       "          x         y  distance_cudf\n",
-      "0  0.431121  0.941755       1.035745\n",
-      "1  0.950709  0.448873       1.051349\n",
-      "2  0.224143  0.067438       0.234068\n",
-      "3  0.644774  0.583203       0.869401\n",
-      "4  0.223024  0.325308       0.394418\n",
+      "0  0.412709  0.196911       0.457277\n",
+      "1  0.753898  0.940824       1.205617\n",
+      "2  0.606489  0.682263       0.912859\n",
+      "3  0.610318  0.000360       0.610318\n",
+      "4  0.374492  0.268757       0.460950\n",
       "\n",
       "HEAD df_w_numba:\n",
       "          x         y  distance_numba\n",
-      "0  0.431121  0.941755        1.035745\n",
-      "1  0.950709  0.448873        1.051349\n",
-      "2  0.224143  0.067438        0.234068\n",
-      "3  0.644774  0.583203        0.869401\n",
-      "4  0.223024  0.325308        0.394418\n",
+      "0  0.412709  0.196911        0.457277\n",
+      "1  0.753898  0.940824        1.205617\n",
+      "2  0.606489  0.682263        0.912859\n",
+      "3  0.610318  0.000360        0.610318\n",
+      "4  0.374492  0.268757        0.460950\n",
       "\n",
       "HEAD df_w_cupy:\n",
       "          x         y  distance_cupy\n",
-      "0  0.431121  0.941755       1.035745\n",
-      "1  0.950709  0.448873       1.051349\n",
-      "2  0.224143  0.067438       0.234068\n",
-      "3  0.644774  0.583203       0.869401\n",
-      "4  0.223024  0.325308       0.394418\n",
+      "0  0.412709  0.196911       0.457277\n",
+      "1  0.753898  0.940824       1.205617\n",
+      "2  0.606489  0.682263       0.912859\n",
+      "3  0.610318  0.000360       0.610318\n",
+      "4  0.374492  0.268757       0.460950\n",
       "\n",
       "Max Difference cudf to numba: 2.220446049250313e-16\n",
       "Max Difference cudf to cupy: 2.220446049250313e-16\n"
@@ -1541,7 +1537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1552,6 +1548,13 @@
     "cluster.close()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1576,7 +1579,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/06_xgboost_trade.ipynb b/notebooks/06_xgboost_trade.ipynb
index 72a661e0..b20fcef1 100644
--- a/notebooks/06_xgboost_trade.ipynb
+++ b/notebooks/06_xgboost_trade.ipynb
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,14 +60,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.11.0\n"
+      "0.13.0a+4804.g6158033.dirty\n"
      ]
     }
    ],
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -184,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -234,27 +234,13 @@
       "        dataframe\n",
       "        \"\"\"\n",
       "        dxgb_params = {\n",
-      "                'nround':            100,\n",
       "                'max_depth':         8,\n",
       "                'max_leaves':        2 ** 8,\n",
-      "                'alpha':             0.9,\n",
-      "                'eta':               0.1,\n",
-      "                'gamma':             0.1,\n",
-      "                'learning_rate':     0.1,\n",
-      "                'subsample':         1,\n",
-      "                'reg_lambda':        1,\n",
-      "                'scale_pos_weight':  2,\n",
-      "                'min_child_weight':  30,\n",
       "                'tree_method':       'gpu_hist',\n",
-      "                'distributed_dask':  True,\n",
-      "                'loss':              'ls',\n",
-      "                # 'objective':         'gpu:reg:linear',\n",
       "                'objective':         'reg:squarederror',\n",
-      "                'max_features':      'auto',\n",
-      "                'criterion':         'friedman_mse',\n",
       "                'grow_policy':       'lossguide',\n",
-      "                'verbose':           True\n",
       "        }\n",
+      "        num_of_rounds = 100\n",
       "        if 'xgboost_parameters' in self.conf:\n",
       "            dxgb_params.update(self.conf['xgboost_parameters'])\n",
       "        input_df = inputs[0]\n",
@@ -270,11 +256,13 @@
       "        target = model_df[self.conf['target']]\n",
       "        dmatrix = xgb.DMatrix(train, label=target)\n",
       "        bst = xgb.train(dxgb_params, dmatrix,\n",
-      "                        num_boost_round=dxgb_params['nround'])\n",
+      "                        num_boost_round=num_of_rounds)\n",
       "        # make inferences\n",
       "        infer_dmatrix = xgb.DMatrix(input_df[train_cols])\n",
-      "        prediction = cudf.Series(bst.predict(infer_dmatrix)).astype('float64')\n",
+      "        prediction = cudf.Series(bst.predict(infer_dmatrix),\n",
+      "                                 nan_as_null=False).astype('float64')\n",
       "        signal = compute_signal(prediction)\n",
+      "        signal = cudf.Series(signal, index=input_df.index)\n",
       "        input_df['signal'] = signal\n",
       "        # remove the bad datapints\n",
       "        input_df = input_df.dropna()\n",
@@ -301,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -345,39 +333,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "id:node_sort process time:0.271\n",
-      "id:node_addReturn process time:1.451\n",
-      "id:node_addIndicator process time:0.241\n",
-      "id:node_volumeMean process time:0.056\n",
-      "id:node_renameMeanVolume process time:0.001\n",
-      "id:node_leftMergeMeanVolume process time:2.728\n",
-      "id:node_maxReturns process time:0.021\n",
-      "id:node_renameMaxReturn process time:0.001\n",
-      "id:node_leftMergeMaxReturn process time:0.035\n",
-      "id:node_minReturns process time:0.027\n",
-      "id:node_renameMinReturn process time:0.001\n",
-      "id:node_leftMergeMinReturn process time:0.055\n",
-      "id:node_filterValue process time:0.184\n",
-      "id:node_dropColumns process time:0.036\n",
-      "id:node_sort2 process time:0.094\n",
-      "id:node_technical_indicator process time:2.799\n",
-      "id:node_xgboost_strategy process time:1.278\n",
-      "id:node_backtest process time:0.002\n",
-      "id:node_training_df process time:0.133\n",
-      "id:node_portOpt2 process time:0.018\n",
-      "id:node_sharpe_training process time:0.001\n",
-      "id:node_cumlativeReturn_training process time:0.625\n",
-      "id:node_testing_df process time:0.030\n",
-      "id:node_portOpt1 process time:0.019\n",
-      "id:node_sharpe_testing process time:0.001\n",
-      "id:node_cumlativeReturn_testing process time:0.493\n"
+      "id:node_sort process time:0.143s\n",
+      "id:node_addReturn process time:0.446s\n",
+      "id:node_addIndicator process time:0.051s\n",
+      "id:node_volumeMean process time:0.109s\n",
+      "id:node_renameMeanVolume process time:0.001s\n",
+      "id:node_leftMergeMeanVolume process time:2.698s\n",
+      "id:node_maxReturns process time:0.024s\n",
+      "id:node_renameMaxReturn process time:0.001s\n",
+      "id:node_leftMergeMaxReturn process time:0.028s\n",
+      "id:node_minReturns process time:0.024s\n",
+      "id:node_renameMinReturn process time:0.001s\n",
+      "id:node_leftMergeMinReturn process time:0.036s\n",
+      "id:node_filterValue process time:0.332s\n",
+      "id:node_dropColumns process time:0.008s\n",
+      "id:node_sort2 process time:0.060s\n",
+      "id:node_technical_indicator process time:3.803s\n",
+      "id:node_xgboost_strategy process time:5.160s\n",
+      "id:node_backtest process time:0.006s\n",
+      "id:node_training_df process time:0.203s\n",
+      "id:node_portOpt2 process time:0.032s\n",
+      "id:node_sharpe_training process time:0.001s\n",
+      "id:node_cumlativeReturn_training process time:2.228s\n",
+      "id:node_testing_df process time:0.061s\n",
+      "id:node_portOpt1 process time:0.025s\n",
+      "id:node_sharpe_testing process time:0.001s\n",
+      "id:node_cumlativeReturn_testing process time:2.452s\n"
      ]
     }
    ],
@@ -404,13 +392,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "adfa373f1c5b47e0b850ec66338fd34f",
+       "model_id": "50de025275284986b59b9b002d1285f8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -452,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -585,30 +573,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "id:node_technical_indicator process time:2.489\n",
-      "id:node_xgboost_strategy process time:3.885\n",
-      "id:node_backtest process time:0.002\n",
-      "id:node_training_df process time:0.054\n",
-      "id:node_portOpt2 process time:0.034\n",
-      "id:node_sharpe_training process time:0.001\n",
-      "id:node_cumlativeReturn_training process time:0.471\n",
-      "id:node_testing_df process time:0.029\n",
-      "id:node_portOpt1 process time:0.018\n",
-      "id:node_sharpe_testing process time:0.001\n",
-      "id:node_cumlativeReturn_testing process time:0.479\n"
+      "id:node_technical_indicator process time:4.281s\n",
+      "id:node_xgboost_strategy process time:9.202s\n",
+      "id:node_backtest process time:0.004s\n",
+      "id:node_training_df process time:0.060s\n",
+      "id:node_portOpt2 process time:0.028s\n",
+      "id:node_sharpe_training process time:0.001s\n",
+      "id:node_cumlativeReturn_training process time:2.107s\n",
+      "id:node_testing_df process time:0.054s\n",
+      "id:node_portOpt1 process time:0.027s\n",
+      "id:node_sharpe_testing process time:0.001s\n",
+      "id:node_cumlativeReturn_testing process time:2.119s\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bfdfde29021d49609b4c27bd5883210e",
+       "model_id": "079b407b3c4c41428c44ba81eda3a78c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -648,13 +636,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d45e1b5cf091436c9c29a0779c74d5bd",
+       "model_id": "f2c589837c6a4f9c8849b545d6f2ed04",
        "version_major": 2,
        "version_minor": 0
       },
@@ -703,7 +691,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/07_fractional_differencing.ipynb b/notebooks/07_fractional_differencing.ipynb
index 14699ba2..64af72fe 100644
--- a/notebooks/07_fractional_differencing.ipynb
+++ b/notebooks/07_fractional_differencing.ipynb
@@ -166,7 +166,7 @@
       "    if isinstance(input_arr, numba.cuda.cudadrv.devicearray.DeviceNDArray):\n",
       "        gpu_in = input_arr\n",
       "    else:\n",
-      "        gpu_in = input_arr.data.to_gpu_array()\n",
+      "        gpu_in = input_arr.to_gpu_array()\n",
       "\n",
       "    # compute the weights for the fractional difference\n",
       "    weights = get_weights_floored(d=d,\n",
@@ -415,10 +415,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "array size 100000, Ensemble: time 0.436 s, gQuant GPU Time 0.397 s, gQuant CPU Time 0.666, speed up 1.10, speed up vs CPU 1.68, error 0.0000 \n",
-      "array size 1000000, Ensemble: time 0.076 s, gQuant GPU Time 0.004 s, gQuant CPU Time 0.031, speed up 19.45, speed up vs CPU 7.91, error 0.0000 \n",
-      "array size 10000000, Ensemble: time 0.628 s, gQuant GPU Time 0.007 s, gQuant CPU Time 0.169, speed up 85.16, speed up vs CPU 22.85, error 0.0000 \n",
-      "array size 100000000, Ensemble: time 5.854 s, gQuant GPU Time 0.049 s, gQuant CPU Time 1.672, speed up 120.59, speed up vs CPU 34.45, error 0.0000 \n"
+      "array size 100000, Ensemble: time 0.404 s, gQuant GPU Time 0.483 s, gQuant CPU Time 0.742, speed up 0.84, speed up vs CPU 1.54, error 0.0000 \n",
+      "array size 1000000, Ensemble: time 0.085 s, gQuant GPU Time 0.007 s, gQuant CPU Time 0.042, speed up 12.07, speed up vs CPU 5.98, error 0.0000 \n",
+      "array size 10000000, Ensemble: time 0.774 s, gQuant GPU Time 0.010 s, gQuant CPU Time 0.287, speed up 78.79, speed up vs CPU 29.26, error 0.0000 \n",
+      "array size 100000000, Ensemble: time 6.987 s, gQuant GPU Time 0.052 s, gQuant CPU Time 2.533, speed up 133.71, speed up vs CPU 48.47, error 0.0000 \n"
      ]
     }
    ],
@@ -498,12 +498,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -630,13 +630,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "63310421c9df4bea9037e1f80e600e3b",
+       "model_id": "04598428378f472b8082afae88dd699f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -679,12 +679,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -735,13 +735,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1c95626aa436451ebe4939cc7fd1ffce",
+       "model_id": "62ff240e01f24dc28e2fa8f74be67832",
        "version_major": 2,
        "version_minor": 0
       },
@@ -807,7 +807,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/asian_barrier_option/mc_pricing.ipynb b/notebooks/asian_barrier_option/mc_pricing.ipynb
index d46f01b5..7efaecde 100644
--- a/notebooks/asian_barrier_option/mc_pricing.ipynb
+++ b/notebooks/asian_barrier_option/mc_pricing.ipynb
@@ -757,7 +757,7 @@
     "                    np.float32(r),  randoms_gpu, N_STEPS, N_PATHS))\n",
     "    v = output.mean()\n",
     "    out_df = cudf.DataFrame()\n",
-    "    out_df['p'] = cudf.Series([v.item()])\n",
+    "    out_df['p'] = cudf.Series([v.item()], nan_as_null=False)\n",
     "    return out_df\n",
     "o = get_option_price(T=1.0, K=120.0, B=90.0, S0=100.0, sigma=0.2, mu=0.1, r=0.05)"
    ]
diff --git a/notebooks/cuIndicator/rsi_perf.ipynb b/notebooks/cuIndicator/rsi_perf.ipynb
index eb8a96e0..f06f8b65 100644
--- a/notebooks/cuIndicator/rsi_perf.ipynb
+++ b/notebooks/cuIndicator/rsi_perf.ipynb
@@ -239,11 +239,11 @@
     "    UpI = np.zeros(len(df))\n",
     "    DoI = np.zeros(len(df))\n",
     "    updown_movement(df['High'].values, df['Low'].values, UpI, DoI)\n",
-    "    UpI = pd.Series(UpI)\n",
-    "    DoI = pd.Series(DoI)\n",
-    "    PosDI = pd.Series(UpI.ewm(span=n, min_periods=n).mean())\n",
-    "    NegDI = pd.Series(DoI.ewm(span=n, min_periods=n).mean())\n",
-    "    RSI = pd.Series(PosDI / (PosDI + NegDI), name='RSI')\n",
+    "    UpI = pd.Series(UpI, nan_as_null=False)\n",
+    "    DoI = pd.Series(DoI, nan_as_null=False)\n",
+    "    PosDI = pd.Series(UpI.ewm(span=n, min_periods=n).mean(), nan_as_null=False)\n",
+    "    NegDI = pd.Series(DoI.ewm(span=n, min_periods=n).mean(), nan_as_null=False)\n",
+    "    RSI = pd.Series(PosDI / (PosDI + NegDI), name='RSI', nan_as_null=False)\n",
     "    df = df.join(RSI)\n",
     "    return df\n",
     "\n",
@@ -314,7 +314,7 @@
     "    :param n: time steps to do EWM average\n",
     "    :return: Relative Strength Index in cudf.Series\n",
     "    \"\"\"\n",
-    "    UpI, DoI = upDownMove(high_arr.to_gpu_array(), low_arr.to_gpu_array())\n",
+    "    UpI, DoI = upDownMove(high_ar.to_gpu_array(), low_ar.to_gpu_array())\n",
     "    UpI_s = shift(UpI, 1)\n",
     "    UpI_s[0] = 0\n",
     "    DoI_s = shift(DoI, 1)\n",
@@ -322,7 +322,7 @@
     "    PosDI = Ewm(n, UpI_s).mean()\n",
     "    NegDI = Ewm(n, DoI_s).mean()\n",
     "    RSI = division(PosDI, summation(PosDI, NegDI))\n",
-    "    return cudf.Series(RSI)"
+    "    return cudf.Series(RSI, nan_as_null=False)"
    ]
   },
   {
diff --git a/notebooks/custom_port_nodes.py b/notebooks/custom_port_nodes.py
index 9419d3e5..d244f5d6 100644
--- a/notebooks/custom_port_nodes.py
+++ b/notebooks/custom_port_nodes.py
@@ -149,15 +149,15 @@ def process(self, inputs):
         # df['distance_numba'] = 0.0
         darr = cuda.device_array(len(df))
         distance_kernel[(number_of_blocks,), (number_of_threads,)](
-            df['x'].to_gpu_array(),
-            df['y'].to_gpu_array(),
+            df['x'],
+            df['y'],
             darr,
             len(df))
         df['distance_numba'] = darr
         return {'distance_df': df}
 
 
-raw_kernel = cupy.RawKernel(r'''
+kernel_string = r'''
     extern "C" __global__
     void compute_distance(const double* x, const double* y,
             double* distance, int arr_len) {
@@ -166,7 +166,7 @@ def process(self, inputs):
         distance[tid] = sqrt(x[tid]*x[tid] + y[tid]*y[tid]);
         }
     }
-''', 'compute_distance')
+'''
 
 
 class CupyDistanceNode(Node):
@@ -201,6 +201,10 @@ def columns_setup(self,):
         }
         self.delayed_process = True
 
+    def get_kernel(self):
+        raw_kernel = cupy.RawKernel(kernel_string, 'compute_distance')
+        return raw_kernel
+
     def process(self, inputs):
         df = inputs['points_df_in']
         cupy_x = cupy.asarray(df['x'])
@@ -208,7 +212,7 @@ def process(self, inputs):
         number_of_threads = 16
         number_of_blocks = (len(df) - 1) // number_of_threads + 1
         dis = cupy.ndarray(len(df), dtype=cupy.float64)
-        raw_kernel((number_of_blocks,), (number_of_threads,),
+        self.get_kernel()((number_of_blocks,), (number_of_threads,),
                    (cupy_x, cupy_y, dis, len(df)))
         df['distance_cupy'] = dis
 
diff --git a/notebooks/mortgage_e2e_gquant/mortgage_e2e_gquant.ipynb b/notebooks/mortgage_e2e_gquant/mortgage_e2e_gquant.ipynb
index ca6bf590..e2151847 100644
--- a/notebooks/mortgage_e2e_gquant/mortgage_e2e_gquant.ipynb
+++ b/notebooks/mortgage_e2e_gquant/mortgage_e2e_gquant.ipynb
@@ -58,7 +58,7 @@
     "import cudf\n",
     "\n",
     "# warmup\n",
-    "s = cudf.Series([1,2,3,None,4])\n",
+    "s = cudf.Series([1,2,3,None,4], nan_as_null=False)\n",
     "\n",
     "del(s)\n",
     "gc.collect()"
diff --git a/tests/unit/test_multi_assets_indicator.py b/tests/unit/test_multi_assets_indicator.py
index d3fa6d66..c4c9e61a 100644
--- a/tests/unit/test_multi_assets_indicator.py
+++ b/tests/unit/test_multi_assets_indicator.py
@@ -95,7 +95,7 @@ def test_multi_assets_indicator(self):
         self._cudf_data['ewma'] = PEwm(3,
                                        self._cudf_data['in'],
                                        self._cudf_data[
-                                           'indicator'].data.to_gpu_array(),
+                                           'indicator'].to_gpu_array(),
                                        thread_tile=2,
                                        number_of_threads=2).mean()
         gpu_array = self._cudf_data['ewma']
@@ -118,8 +118,8 @@ def test_port_macd(self):
         '''Test portfolio macd method'''
         n_fast = 10
         n_slow = 20
-        r = gi.port_macd(self._cudf_data['indicator'].data.to_gpu_array(),
-                         self._cudf_data['close'].data.to_gpu_array(),
+        r = gi.port_macd(self._cudf_data['indicator'].to_gpu_array(),
+                         self._cudf_data['close'].to_gpu_array(),
                          n_fast,
                          n_slow)
         cpu_result = ti.macd(self._plow_data, n_fast, n_slow)
diff --git a/tests/unit/test_nodes.py b/tests/unit/test_nodes.py
new file mode 100644
index 00000000..39974eba
--- /dev/null
+++ b/tests/unit/test_nodes.py
@@ -0,0 +1,174 @@
+'''
+Technical Indicator Node Unit Tests
+
+To run unittests:
+
+# Using standard library unittest
+
+python -m unittest -v
+python -m unittest tests/unit/test_nodes.py -v
+
+or
+
+python -m unittest discover <test_directory>
+python -m unittest discover -s <directory> -p 'test_*.py'
+
+# Using pytest
+# "conda install pytest" or "pip install pytest"
+pytest -v tests
+pytest -v tests/unit/test_nodes.py
+
+'''
+import warnings
+import unittest
+import cudf
+from gquant.plugin_nodes.transform.returnFeatureNode import ReturnFeatureNode
+from gquant.plugin_nodes.transform.indicatorNode import IndicatorNode
+from gquant.plugin_nodes.transform.assetIndicatorNode import AssetIndicatorNode
+from gquant.dataframe_flow.task import Task
+from .utils import make_orderer, error_function_index
+import numpy as np
+import pandas as pd
+
+ordered, compare = make_orderer()
+unittest.defaultTestLoader.sortTestMethodsUsing = compare
+
+# gt for  return
+ground_truth = b'\x92\x01\xef3\xec \x02@\xa3\xd5\xc0\xd96\xdd\xc0\xbf\xe2\xe1\xa7f\xab\xc0\xaa?\x8e\x9cySv\x9b\xca?vA\xc5\xd9\x8e\xd6\xd8\xbf\xbf){\x92=p\xc3\xbf\xd7\x06\xda\xca\x98\n\xd0?\xcf\xbf\xe8C\xb8\xb5\xc6?\x03\xa9\xf2H1\xcf\xca?V{\xc3\x9c\xda\xd5\xc0?\x1f\xed\xc6p\x14\xea\xe4\xbf\x84*\x911\x07\xf6\xe1\xbfQ\xdbQON&\x12@b\\H\xceZ\x19\xd3\xbfsxy\xfe\x9aT\xdf?\xc08\x16\xc2\xe0\xef\xdc\xbflI\xa2\xf0\xbfo\xee?\xc7\x0f\xffY\xa0\x00\xd7\xbf\x04l\xf3\xfcD\x16\xe1\xbf\xb9j]\x90my\xe8\xbf[\xc0B\x82\xe6\xf5\xe1?h$\xf1\x99\xec\x9f\xec?\xf1#\xdb\xb9GX\xf8?\xcd\xaf\xe4\x1b\xc0\x13\xeb?\xa8`\xa6\x8d\x8af\xbf?m\'\x07\x92j\x80\xd7\xbf<\xa4\x04\xfb\x93\xa7\xdf\xbf\xd3\xdd\xbcGk\xd1\xf3?\xf9"\xc8w\xad\xc8\x95\xbf:3\xe3\x05\xef\xf4\xca\xbf\nU\xd1\tQ[\xed?\xd3\x8f\xc0\x87J\x0f\xc5\xbf\r6\n[\xb1T\xc3?\xd5I\x83\xa6\xee\xae\xd5\xbf]\xc3\x1f\xc6L/\xe3?I\xe3K\x1b\xf6f\xd1\xbf\xaf+ =\xc9\x9c\xef\xbf{\xd6\x94\xb3R#@@\t\xe8\xe6D\x83\xb8\xfe?\xc2\x01\'3NJ\xde\xbf\xea\x98j\xdc9\x81\xe3\xbf\xb2.\xf1\x9e\xcd\xa4\xd5\xbf\xf2Q\xbf\x0c\xf1\xdd\x17@\xf4\x05\xf44>\xe6\xc7\xbf$\xfc\xee?&\x07\xcd\xbf\r\x1d\x0eg\xb6\x90\xdb\xbfu\xbe\x9d\xde\t\xaa\xf3?\x18\xa0\xb6\x11\x83\xf9\xda\xbf\x97X=\xaa\xe5\xbe\xdd?\xca\xf6\xcb_re\xda?\xdc\xc3\x92tf$\xba?\xc5\xd6{8G\x00\xec\xbf\xddo\xd4\x8c\xc8\xeb\x0e@\x06\xd7\xfe\x9c\x00\xe7\xd0?\xca\x90F\x90gi\xe8\xbf F\x85<\xd3\xa1\xef\xbf\xd6A\x15\x00\x8a$\x81@/\xfbQ$\xbb\x11\xc5\xbf\xaf\xfeh\x1f\xe0$\xe5\xbf?o\x97\x9b\x87\xc3\x92?\xc4{\xe6\xbd\xbd)\xe2\xbf\\\x16\x0fp\x8b\xdd\x07@y\xa0,;\x81\x10\xee?-\x94\xe8C]\x15\x93\xbf\xd4b\xc0gix\xe7\xbf\x9bm\x8c\xe8\xab\xcf\xe3?m\xa9\x12H\xa0P\xf7?\xbf\xc3\x11\x0e\xde\xe8\xe6\xbfT*=\xf3\xb78\x03@\x8aY"\x8c\x17\x0b\xc7\xbfOy\xe8sPB\xc3?\x98j\xe1\xbd\xcd\xbe\x8f\xbf\xb0W\xa1Dh\x9e\xea\xbf\xc0\xa3\xb1\xb4U\x8d\x11@4\xfc\xaaY\xecw\xc3?\x90\xde):Y\x01\xb4\xbf\xc5\x15\xfdg`C\xc3\xbf\xae\xa3\x88\x92\x90/\xe2\xbf\xce{^\xef\xfdR\xc3?\xb4\xbeN\xd5P>\xd5\xbf\x97\xd9e\xc3!\xd7\xe3\xbf\xd7+#\x11{\xa5\xf2?1\x1b\xc6^\xeaT\x01@\xe8\x1eV\xa0^o\xe0?\xfb\xc7\x92\x02@\x8c\xd0\xbfm\xfa\x9fef\xc0\xc7\xbf\xc7\xf0\xf4r%\x01\xe1\xbf\r\rv&\xac\xf4\xef?vh\xd4\xfcQ\xd4\xe0?\xde\xa3\xab\x04\xe3B\xe4\xbf\xfd\x03)\xaa.<\xd8\xbf\x89.\x8c\x9aN\x8e\xec\xbf\x0b2M\xb3\xc1UB@\x16\x80^\xcc\xf9Y\xee\xbf[\xb63\xe7}\xa6$@|\x0eb$\xaee\xed?%Y(\x9b\x7f{\xef\xbf\xb10[\xd1\x1flB@\x06\xaf\'\x19I\xf1\xad\xbf\xea_\xa2\x15\xf0\xaa\xe3\xbf=\xd9q\xa9U\t<@>\xef\x06{~\x13\xea\xbf\xce!\xaf\xce\x80y!@\x17*T\x8ae\xc9\xde?\xcc\xe4\xa01\x0c\x1d\xe1\xbf\x18\x9c\xc1\rC\x07\xed\xbf"k\x95 \xfc\xcb\xf1?\xc9e\xf6b&)\x8b?\xe6^\xd1\x03\xe2\xc2!@P",%^\xf2\x85\xbfV\x06\x94]\xfeR\xef\xbf\x9d\xfa\x88\xad\xa0\xf7H@\xf5\x84\x17;\xe9\xb9\xad?$|\xa9\xb1`\xec\xe1\xbfW\x8fd\xd3\xa72\xd2\xbf\xe9\xf3\xd8!U\x04\xff?\x8d\xec7d)(\xd1\xbf\xde\x06C\x7fb\xb5\xe8\xbf\xccF$ux\x10\xe4?E\xc2\xcbD\xc0m\x05@\xceS\x8c1\xd8\xd8k?\x18\xce\xa7z"\xa8\xe7\xbf<s8M\xf2y\x05@\x0e\x1aK\xe8b\xb6\xd5\xbf_O\xb0_^\xc9\xc1?{\x92*\x07~&\xc2\xbf\xcf\xac\xcc\x80\xbaH\xcc?\xe3G\xe6\xe1\xf5\xbd\xef\xbf\x7f\x82\x84\x12\xc2a\x0e@\xa7G^\x88z\xe4:@\x9c\xd1\xf3\x92\xd8\n\xe8\xbf\x8b@\xa2\xa66Z\x08@2&@\xd9`\xd8\xe2\xbfj\x08\x1f\x9e\x84\xd1\xe9\xbf\x8f5\xc9L\x8c!#@UX\xa0\x07\x89{\xdf\xbf\\]\x89\xfe\x08]\xb7?\xe7\x92B\xcb|\xed\xff?\xe2b\xe0\xb3\x94\x8c\xe9\xbf\x80H\xbf\x93\x1b\x07\xe9?2\xf6u@;k\xe6\xbf\x97.e%\xf1\xe6\x06@\x8f\xf9\x91\xc9\xe9\x9a\xe8\xbfU7\xa1\xe7\xd4\xbf\x11@L\xadA\x91\xbe#\xde?\xcf\xba\xfc\x0f\xa3\xc5\xef\xbf\x7f+\'_\xc4\n0@1=;4M\x1e\xd8\xbf\x060E~\xc9z-@3y\x08I<P\xbd?\x8d\x02\xbd\xf2\xd08\xde\xbf\x8dU\xc4I6\xcb\xe3\xbf\x0bc"\x89\xb7\xa1\r@\xc8\x17\xf79.\x1d\xd3\xbf\x8cyq\x04\xa9\xd3\xca?`\xb2\xb5R\xc4\xad\xd6\xbf\xfcF\xc90\xbdV\xe1?\x14\x94D\xc3hh\xe2\xbf\x8e|\x0cY\xdb,\xe5\xbf?\xe7\xcf\xb3\x0c\xf5\x1b@\xb9|\x12\xef\xc1\xd9\xef\xbf\xdd\xf1U2\xdff0@\x88\xff\x1e\xe9\xdb\x97\xe9?\xab\xdc\x95\xc1\xb3|\x18@\xba_\xc38\x06\xb6\xe5\xbfK\x05\xd9\x0c\xee\xee\xd2\xbf\xaf\x0c\x85|\x80i\xc9?\xcc\xd0\x90\xab\x81\xfe\xe3\xbf\xadEW\xcb\xd4\x82\t@\x10\xd2\xeett8\xea?\x91\xc9R?\x89\x00\xbd\xbf\xf7R\xb0\xe9\xd7\x9d\xe3\xbf%\x1eK\xcc;\xe3\xb7\xbf\x1ce\x80\x92D\xe1\xf2?XO\xaf\x94\xdd\xcf\xe7?iB \x9e\xe8\x12\xd1\xbf\xd5B\xf0t\xadO\xc8?>\x89x\xa3\xc1\xeb\xda\xbf\r\x1c\xf50\xec\x9d\xb5\xbf\x95FF\xe8z\xcd\xe9?}^\xbd\x94\xba\x08\xdf\xbf\xdd\xf3I\x96\xfd\xf9\xde?\xdcl-c*W\xeb\xbf\x86\xd4\xfe\x0b\xe6\x8a\x19@n\x9f\xf5\x1c\x8a\xdf\xe0\xbf`\xa2JiRI\xfa?\x88\xfb\x95\xcb\xce\x1e\xe3\xbf\x01q\xf5V~\x8f\xf8?;\xd6\x8dX$p\xe6\xbf\xec*\xb9\x9f|`\xef\xbfI\xbd\xbd\xa5\x90\xb6>@\xc4\x92\\\x81\xcd\xda\xf1?\x9c\xcdf\xe3\xf7\xac\xd5\xbfH\xc6\x8d\xbbU\xe6\xfc?=i\xb19\xe4\xc1\xe3\xbf\xa5b\xc3oo\xa8\x05@\x97\x17\x10\x8e\x84\'\xd8\xbf\x8f=z\xe6\x1al\xd4?'  # noqa #E501
+
+# gt for indicators
+g_t1 = b'\x80x\xe2\xf8Q\x0f\x7f\xbf\x04O\x11|\xec\x0e\xdd?\xa0\xbf\x98\x96\x03\x97\xca?\xf7\xbbF\xea;I\xd0?\x02\x8e\x80\xa6n\xf5\xcd?\xf85`\x18)\xd1\xc3?/\x12n\xb1\xb1\xaa\xc8?\x88^c<\xeb\xe5\xa3?d\xce[\x8f\xd73\xb0?$\xfe\xba\x87\xecq\xb2?(\x87\x87\xfcTF\xbf?\x1ezq\xeee\x13\xd2?\xccPQ\x90\xb4\xf5\xcd\xbf\xa8\xd0\xfd\xf9\x1c\xfd\xc1\xbf\x92\xfdI\xf3\xb6\xdf\x05\xc0m\xd5\xff\xd84\xb4\x00\xc0\x8c)\x9c\x0c\x86\x08\xf6\xbf\xb0\xeb\xb4x\x83\xad\xea\xbf\xf4\xdc{HY\xe3\xe1\xbf\xc0j\xb9I\x8c/\xce\xbf y\xe5\xe1\x15\xa0\xb4?L\x0b\xactJ\xe6\xd2?\x14i\\\x0c\xae\x1e\xdc?\xb8\xe2\xc0\x98\x19\x19\xd8?\x8c\xfa\x1fRG[\x06@:\xa4S\x87\xfek\x02@\xf5w\x95\xf5\xc6{\xfc?\x9a\xa5\x94Z\xee\xdc\xf5?\xfa7;\x04\xc7\xc9\xf1?\x95QY\xa3\xa3\xa3\xd2?\xc7\xfe\xe6J\xfb\x84\xa0?f\x1d\x97G\xd3s\xd7\xbf\x14\x11;z\xfbX\xc1\xbf<\xd0<\xdb\xf2\x01\xb8\xbf>bq\x8aG\x18\xc7\xbf\x98\xb0\x83\xf6\x16L\xc0\xbf`\xb9\xd6NE\x9d\x9e?GT\xdb\x9cT{\xd0?\x1a\xc1\xa9\x86\xa7\xe1\xc7?Z\xa0\xf8\x7ff\x0b\xb3?(\x8bT\\\xb2\xcc\x7f?=\xceH\xdckF\xd4?D\x1fV\x8cHG\xd6?2B\xcd5\xa3\xb1\xd0?\x8aI\xe5Kt.\xc4?Ta<\xe3$\'\xbf?p\xc5\x8b\xb5Yu\x8e?\xf0\xe0(\x8a)=\xa0\xbf4\xe1\xf02u\x85\xc3\xbf\x18\xb6\xbbYrm\xc5\xbf\xc6\xc2\xe0BV\x1d\xc5\xbf8\x8cO\xb3\xbfH\xb4\xbf\x1c\xfe|Yt,\xc4?\xd3\x99\xc8b=\x02\xc0?\xac\x99\xe6\x00\xb9\xb9\xc1?\x04U\xd9\x98\xc3\xa5\xbf?\xbeRe\x1d\xba\xe5\xc4?\xa80o\xfc#\x12\xbb?p1\\\xc4\xb5\x86\x9a?\xe4\x19\xe2Ch\x9d)@_\xc7\xe8;\xea6"@\xba\xac\xae\xa0\x8aA\x1e@Xgp\xb4\x90\xdfM\xc0$\xacd\x8d\x11\xd1E\xc0`\xf4\xf3+p`?\xc0\xba!\x19\x10\x8aZ5\xc0X\xc9\x8e\xdc\xd1Y+\xc0\xd8\xc8\xab\xd8] \x1f\xc0\xb0\x03\xac\xd4\x1a\xa1\t\xc0\x80\xf2g\x98\xa5h\xcd?\xe0\x8b\xf8\xda\x899\x05@8D\xba\x13\x1b\xf5\x11@\x0c\xd0\xe2\x95\x80\x93\x16@\xe2\x95X\x12\xd8\x13\x1a@\xe6Yu|\xd0`\x1c@\xc8\xa3h\x9e\x1c\x94\x1e@\xdcVI\x80\xb9\xb5\x1e@\xe6\xe1\x07\xcaK\x82\x1c@G\x1d\x81\x85\xd0\xbd\x1c@\xa1\xce\x95\x87e\xec\x1b@\x9e"\x88\x91\xab\x90\x1a@\x02!\x9f \xef\xf7\xef?\xf6\xa1l\xc5\xafG\xf0?\xb3\xf6\xef\x14\xfc5\xf3?\x8e|x2\x9bj\xf0?j+\x01B\xa3\xb9\xf2?V\xcc\xc6i\\\x07\xf1?\xbf\xd6S\xde\x15\xdb\xf1?B\xcc\xf6\xd9\xc6z\xf0?\x88\xc1\xa0~\xcco\xed?$Uv_\x94\xe8\xe2?\x82\xd7V\x9f\x8f\xc5\xdc?\x1f\xa5;\x85\xb9O\xe4?\xf0X*\xe7P\xd8\xe1?\xdb+\x1c\x9c\xa0(\xed?A\x8f\xc5e\xb5\xf7\xe7?\x9c,7\t\x84\x87!\xc0\x14\xaa9k\xca\xd6\x18\xc0\xc8\x82\xe1D\x9d\xfa\x10\xc0\x82\xc4\xe5\x0e:E\x04\xc0\xfc\xbf\xc7\x0c\xc5\xd5\xf6\xbfP\x86\xbf\xd5]K\xdf\xbf@{D\xfb(:\xce? 0\xd2\xd0\xecv\xe3?(\xca\x1a\xb9\x06\xa3\xea?\xd8\xb11\xa7L~\xf1?\xc81\x0f\x86\x82v\xe5?\x94\xbd\xa5\xce\xa1.\xee?\x80\xef$\xe0h\xeb\xf4?K\xe5$\'\xf9\xf5\xf5?6\xb9\xc2\xdc\x9ev\xf6?t\xc3\xd2\x16ZZ\xf0?\x94q\xff@\xa5\xd4\xf3?W0\xed\x1a\x96N\xf4?\x90y4fk\x8d\xf4?\x1e\x8c\xed\xb6O\xd7\xf2?\xa4\x17\xdcJv_\xf3?\xfd\xe7\xff\xbb#P\xf3?\x80\xcbD\x16\xc6\x8f\xe0?d/*\x1f\xf5\xba\xe2? \x96\x86\x16\x0b\x9b\xdc?\xae\xbf\xc4Vm}\xe0?\x8b\x0e/\x15\x87\xb9\xe0?tU(c\xa3\x92\xe2?\xf9\xbet\xedd\x05\xf1?\xf8\xb5\xeb\x03\x03,\xed?\\\x01\xcd\xd3h\xc2\xd8\xbf\xb0w\x04\x91\xf3\xb8\xb6\xbf\x00\xe3B^\x03\xcf\x83?P\x10\xe5,O\xee\xb7? ^\x9e\xdct\xbf\xcb?\x90\x98=u\x9aD\xc7?\xf0QYc\x94\x86\xb8?\xd8\x87\x02\x81\xf4I\xc2?\x90?$jU\x14\xd1?\xdeIs&\xf7\xe1\xd4?D9\xee9\xc7\x07\xcb?\xe6M\xf5:\xa7\x84\xd3?\xe0\nv\x97\xaf\x18\xe4?\r\x9c\xf8\x97\xba\xdc\xe1?\x989\xe1\xf4\x96\xe4\xe0?\x00\x1b\xc8\xf0\xe4\x87\xde?,^\x04$\xf4\x1b\xda?p\x08\x8c\xdd\xd9\x97\xd7?\xfd\xb3D0=\x14\xd5?\xe0B\xe6+\x04V\xd5?\xbe\x8dx\x7f\xc6\xf7\xe0?\xc1\x87\x12`\x07~\xd7?\x14\xadB\xa8\xf3$\xff?\xd4:\xf3[u\xb5\xf5?\x82}\x00^.,\xef?\xb0F\x11\xac>\x8c\xe6?\xc0J\xfc\xa1\x03\x9c\xdd?\x90\\E\xe5\x05\'\xce?h)\x95\x9a|\xe9\xb5?P\x13ar\x88\x92\xa9\xbf@\x93\xdd\xb0iF\xbe?\x00?\x14\xa4\xe3 \x84\xbf\xac\xea\xf7\xed\x91D\xc3\xbfD7\'\xdf\xab\xaa\xe4? \x8e\xe2\xb9\x87\x9d\xb3?\xc0\xb2}\xbf\xfd \x95\xbf'   # noqa #E501
+g_t2 = b'&\xec\x1b\xc9\xee\x0b\xff?)\x9es\x15\x01\xff\x02@D\xec\xda\x93\x9ey\x05@4\xee1\x83s\xfc\x04@U\xaa\x1e\xd4\xc2\xfb\x04@Z\xd2\x08\xb1Q\x93\x05@\xef\x81L_uB\x06@\x1d\xa95\xfc\xee\xb5\x05@\x91\x88\xb1\xbe\xa5\xae\x06@g\xaa\x91\x1bn\xa2\x06@\x08\x08\x16h\xff\xbc\x04@\x99\xa6\xcf6\xc5\xab\x00@\x10\xdc\xcb\x1cq\x82\xfb?u\x91fh\x1e\xe6\xf4?\x97\x01\xaa\xa4\x0bu\xf3?7\xac\xf5E\x86\xe1\xf3?\xc7\xa8l\x95\xf8\x86\xf4?\xf0\xa8 z\x10\xc4\xf3?$\xa5k\x1ds\xf1\xfa?y\xf4=\xad\xa2\x0b\xff?\t(\xa4{y\xad\xfe?\xe1\x89\x8d\xe8\x9cW\xff?\xaa\xb5S\xf6\x00\x8e\x02@\x92\xceA\xac6@\x06@\x82\xd1\x1e\xa7%\xdf\x05@|\x894\x0c\xa6\xd0\x05@\x15d6\n\xf6\x01\x05@\x99\xb3KI U\x06@\xfa\xe7\xfa&\x88K\x01@!G\x87|{\xa8\x00@\x9b\x9f\x8e\xbb1\xfd\xfe?\x07\xcf\xfb\x8cJ\xba\xff?\t4)6u\x86\xfb?\xf6\x85 "\x89\xcd\xfb?\x91\xed0\n\xb8I\xfb?h\x9b/\xdfkG\xfb?\x1f\xbd\xa6\xd5\x0c\x84\x00@Z\xa5\x1c3\\:\x04@\nc\xd1\xafbm\x05@2~k\xfeAV\x04@\xfc\xb5\xc7\xcb\x05\x04\x06@[\x02\xd6-M\xec\x07@l\xc5\xb0\xcf+<\x0b@R\xdb\xb4#r\xb8\x07@\x99\xfb\xb0\xa2\xee<\x08@\xd6\x7f\x0b\xed\xe7\x82\x08@:l\x9a\xad\xac\x8d\x07@\x0b\xf0\x84\xa7+t\x03@\x8a \xf2\x100x\x03@\xe2D\'{\xd3F\x05@\x92\x96\xb5}y\xcd\x03@X\xe8\x02\xc2\x7f\xb9\x01@\x84\x8e\x8c\xe4\xeb\xf0\xfb?w\xcb\xd4\x11R\x1c\xfa??\xab1\x06\xb4\x9d\x00@\xf5\x9fz{\x18z\x00@I\xb2\xd6\x8d\xab\xf3\xfb?\xb0y\xea\xe9\xbe\xd2\xf8?z\xb4s\xb0)\xab\xf8?\x9c+\xc5\xe7\xa6\x11\xf8?>\xce\xa7\xf9\xc9(\xfb?\xe4\x8a%_\xb6\'\x00@6\xff\xea s\xec\x03@\xacY+\x86A2\x06@\x8cu@\xd6\xc5\x0f\x03@\xc6\xf6\x97V\xe5\xa3\x03@J+\xc9j^\x04\x03@3m\xffeG\x89\x02@\xe2i\xc5\x7f.\xec\x03@\n_Z<\xe7\xd1\x02@\x8d\xfd\xd6\x1e@h\x02@dWl#\xdf\xd8\x01@\x8d8\x9b\\\x93\xa1\x00@\x97\xd7\x7f\xa9\x0e\x04\x03@\xcd\xc1C\xb00\x18\x03@\xfd#\xb0\xba\x03\x82\x06@\xef\xa6\xe8K\xef\xa4\x06@A\xd5\xec\x06B\x8b\x07@\xe6\xd3\xf1\x0b\xfb\xf5\n@\xab&\x96a\x17\x05\x0b@\x9d\x10\x87\x96\x10\xa9\n@BN\xda\xd5\x01\xa3\x02@\x85T\x8cE\xad\x01\x05@@5d\xe0\xfc\x18\x05@\xf5\xdb\xeaf\xd4@\x00@:\x7fJ5l\x8d\x02@\x8a\xeb\xc7^\x1e)\x02@m\xd8\xb6\xe8J\x84\x01@\xac\x15PM\xd7\x92\xff?\x9d\xc8m\xc7fn\xff?2s\x95\xd4\x9df\xff?\xd7CM\xaf\x92\x9f\x01@Id\xe8uM+\x04@\x10wT\xf6>\xd9\x03@F\xb3\x04\xb6\xd5\x08\x06@4\xafo\xe6\x95\xf0\x03@\xbc\xd4\x1a\x04\xd6\xcd\x04@]\xcam\xc1m \t@K$r\xb2\xc9\x1a\t@\xe4\xe5\xe8\xa3AR\n@\xd9\xc4\xec\xb5\xdd\xf9\n@\xf7\xe1Y\xea{H\x08@^f\'\xe6\xe1\xdb\x05@\x01\xa1:\x19\x96\x90\x06@\x9c\\\xed\xe89\x85\x07@\'\x9e\xf4\xdd\x19\x8c\x07@\x1e%}\x9f\xe2\x15\n@\x1bd~\xad\xf4\xc4\x05@cN"\xad\x8fY\x06@V\x17|2\n[\n@?^3:\xa6K\r@\xaf2\x16hW\x0c\x0e@D\xcc\xef\xf7\x83\x99\x0e@>\xce\xe0\xb2\xc0\xe8\x0e@\xdd\xd82x\\\x02\x0b@\xe7\x97Ya\xac\xed\x0c@\xfb\x1fd2\xea\x00\t@LN\xff3\xa7{\x08@,S\xdfK?y\x08@~\xb7E\x7f+\xdd\x03@\xa77\xdcX\xb1\x95\xff?\xff\xbet\xafW\x8d\xf9?\xb1\x92@\x1f7\x83\x00@\xe7\x9c^\xab\x1d \x00@\xf3\x924\x07l\xe6\x04@\xa4l4\xd6\xfa[\x06@\x1aceu\'X\t@\xff8P\xean\xf5\t@\xebc\xaa\xef\xf4\xbe\x0b@\xb1R?d;-\x0e@\xf1\xbaq#\xdd"\x10@E\x94\xbb\xf3\x12\xd3\x11@\x98c\xaez\xb8\xff\x0f@\x9f\xbe*\xde\x08\xb7\x0e@#\xdfof\xf7\x13\t@\xd0\x15\x91(D\xb8\x06@\x11\x046o\n\x86\x05@\xee\x1e@\x11&\x0c\x02@0\x82}+j\xa1\x03@\xd5$\xcd\x1b\xbe\xa2\x01@&?\xd5&\x97-\x00@\xfb\xebg\x97<m\xf9?D\xc4\xf5\xff\xa5r\xf9?\xc6\xbe\xbf\xef]e\xf9?\x18l\x92e\\\x18\xfb?N \x1e4\xaf\'\xf7?sT\xc6\xd8\xd3[\xfb?\x1f\x94\x1c \x05\xe9\xfa?\xeb\xb0s\r\xb0\xc6\xfb?\r*S\xfb\xc8\x9d\xfc?\xc3\xd7\x18\x91\xa3\xa5\xfd?\x94\x97s\xdc.\x8f\xfe?0K\x8b\xc7\xfc\xbf\x00@\xc1\xd6\x90\xe4qw\x05@|\x8aG\x8c\xa3\xdb\x07@#\x9f6\x95>\xf7\x08@W\xfc\x81\xf3\x979\x07@\x8b\x82\x9b\xa2r \x07@\xb7\x7f\x8f\x85\xab\xe5\x07@\to\x19\xb8\xd9>\x08@!}\xb8\xd8,C\x07@\x8d5\x16\xcb\'u\x06@'   # noqa #E501
+g_t3 = b'\x95\x13\xbek\xd1O\xcc?\xf5\xdc\x9cSD\xf6\xc0?&\xb1\xee\xdb\xdcD\xca?\x91\xf4\x90\x17\x87*\xd1?\'\x8b[\xfe\x98\xc4\xe0?S\x84 "\xca\xeb\xe9?\xdd\xea\x80\xd4\xe7\x8c\xeb?\x14@]\xc6\x91\xb2\xe2?\xc8RnQ\xacL\xd9?\xdd-tK\xb8\x03\xe6?\xea\xa9\x81U\x18\x9e\xe4?\xce\xc2\x8f\xa3`\xb0\xdf??\xd4\xb9\x0f0\x89\xeb?\x08\xe7\xe1k\xaay\xe5?\x08\x14:}\xe7.\xe8?\xa4y\x17\xae\xe9j\xda?\xf3\x05\xac:uC\xea?\xab\xf9]B;2\xe0?\'\nYVy\x17\xb6\xbf\x0f\x85<o\xa5\x80\xcb?S\xc4\x00\x95\x86j\xe4?\xd8\xfa\x03\xdc0J\xd5?\xd79\xec[\xb8F\xc9?%s\xed\x94\xfbK\xcc?\x0c\xfa\x91l}\x0e\xe7?)b\x90D\xc4t\xe3?\x87q\x83\x1c\xceM\xe1?~\x13Z\x07\xaf\x08\xd9?\xae\xb6\x0b7$\x1e\xe5?\x0e\xec\x82\xcfe\xcb\xd8?\xd5/p\xdd\xe6>\xe3?\xb8\xa4\xf0\x8c\x0fU\xe9?\xe8-\x9f\xb4&\xd4\xe9?=\x19\xa57\x9b\xba\xa1?~\x12F!\xceU\xdf?\x19\xde\xdb\x85G\x8d\xe4?\x18vO\x0c\xd6\x94\xc5?a\x03\xc1R\x00%\xbb?\xaa\x02\xbc\xa4\x92\xaa\xeb?B.\x0eI\xb9{\xe6?%X\xdf\xc4\xbeR\xd5?\xf5{\x86}\xf1\xa0\xd7?\xee\x16\x05\xfb0\x12\xd3?\xde\xdbF\xea\x1aq\xe1?\x0cT}M8\x8f\xea?\xa6\xf1Ik\xbf\xee\xe8?_\x02\xdaMV\xd1\xd4?G\xcd\xbd\xa2\x0c\xdf\xd8?\x01H\xb1\x9c$e\xea?\xfc\x15\x1a\x86t\x82\xd5?\x94\xc7\xe5\xff\xcb\xea\xe8?]9\x88\t\x1a\xf2\xe3?\xf8\xe7\xb1\x8a\x97\x80\xe5?-\x97\x0b\xbc\x88!\xe4?z\xa4\x12\x0b<u\xc0?\xd2*O\xc3\xa1\xfa\xe3?\xfc\x1d5G\xf9\xa6\xe5??\x10\xcbp^\xc3\xe2?\xe1\x85\xb1\x10\xab\xec\xdf?\x9d$\xd4G\x07\xd4\xbe?\xc6\xbcI\x8c}!\xcd?\n\x8f\xf9\x93Mb\xc9?\xfaV\xb1\x89\xefc\xc5?"eE\x80\n\xbc\xd2?\x97\xdf\x12\xc1\x87\xf5\xe2?(\xcb\x1c%\xde`\xea?)\xac\xe9>\xd1@\xe5?\xb0\x83\xca\xf0Y\xbe\xe2?+\xd7\x1b\x8cmB\xd6?\xc5f\x01T\xfcr\xe2?\xe3x\xcb\x9d5\xd7\xe8?De2\xce\x81R\xd4?\xf4\xe3\x8f\x96\x9f\xb1\xc8?*N\xb7CD\xa4\xb8?h\xcbs%\x1fo\xe6?\xa8\x9d\xff\xd9b\xb9\xc6?\xabpb\xb4\xc5[\xe1?\xdd"U\xb8\xec\xf9\xea?\xcd\xd2\xad:9U\xcb?pk\xceP~\xfc\xe2?\x83\xce.\xd1l`\xe3?\x02\x01\x7fy\xe8\xa2\xc6?F`\x00\xa1\x10\x8b\xd2?*\xcdx\x13\x19\xb3\xe7?\xb6\xff\xf2\xe5E\x81\xe6?\x95\xaf`\xebB\x95\xcf?\xef\xeb\xa7\x7f}\xbf\xe7?\xa2\x86\xd2\xba6\xf2\xdf?\x97\xe6\xb1\x19\xef+\xe1?B\xaef\x9eU.\xdf?\xaeI\x1c)r\x05\xe3?a\xbf\x960\xdc\xdb\xa9?\xd85\x15\x05\x1fm\xbf?\xf0\xe4Q\xeb\xaf>\xe5?1\x07\xb0V\x08/\xd2?\x8c\xdc_\x84F\x17\xe6?\xb0%\xb4\xfd\xbf.\xd8?CE\xca\x8e\x08Q\xcd?\xce\x85Sv+\xf9\xe5?\xc9\xdbP\xc82B\xdd?\x97\x8c\xf4\xdb\xdaN\xe0?\xa2\x18\xe9\x13Gd\xee?\xb0\xeaT\x8djL\xd3?\x1c\xbf\x81\xe78p\xdd?\xe9N\x13\xb2s\x1d\xd0?\xc0y\xdd\x15%i\xe1?\x9b\x1d\xa3\xa9(\xca\xd1?\xdf!\xa0\x93\xb7\xce\xe3?_A}\xb0\xff\xf2\xe9?\xab\xc0\xb0\tq\xb4\xc9?\xb2\xda\xder\xd5\x16\xd3?\xe0Tl\x81\xcc\xba\xd2?\x95\xd8\x94l\x04\x8b\xee?\xa1\xbd\x9a\x0c\xd1\xd2\xec?\x87\x06\xd1\x06\xf4\x07\xe2?E5\n\x90R\xe8\xd6?\x08\x95\x03\x1aK.\xe9?\xc1\x83\xc7\x1d\xcf\n\xe3?\t\x0c{\x94q\xb7\xe5?e\xcaA\xd0\x8b\xa5\xdd?\xc0\\2y\x91\xfe\xe3?\x1b7L\x05\x0c~\xc8?\x00t\x83\x01\x90\x0f\xbc?_\r\xab\x02t\x1a\xe9?\x1a;"V\xf8\xcc\xbe?l\x14\xc2\x0c\\\xca\xc8?\xae\xcb\x85\x8e8\x00\xd2?\xd2\xa9\x93\xbfgZ\xea?\xe1\x19{\t\x0f(\xdb?W\x81^\x89\xa8\x88\xd8?\xd0c\xb3\xf6>\x83\xdc?\x0e\xabQ\xc4\x1c,\xd6?t\x98!\x1d\xa7\xa9\xe1?@M%\xc8\x92\xfd\xea?luH\xbd?8\xe7?\xc4\xb86tT\xb0\xd7?n\xbe\xbb\xa0\xed\x81\xd5?\x1c\xd9e]G:\xe5?\x0e\xed\xe1\xf4K\xe8\xed?Om\xd4\xb1\xffw\xe5?\x01\xb1\x1a\x00\xdb\x90\xe7?\x88\xaa\xdc\x1b\x00\x0e\xd8?K=\x0c\x02\xa9W\xd5?\xb6\xedl#\xa4\xa6\xe6?\x8e\xa4\x84\x05\xdfz\xd5?$\xecR\xc3\x85B\xe0?\xe5b\n\x83@qn?T\x17v\x80h\xdc\xe1?\x1bk<\xfc\x0e\x94\xd0?\x8a\x9b\xab<\x9f\x02\xe9?\xd2\xe7\x86\xef\xf1|\xd4?2\xbb\xf1\xc3X\xac\xe9?\xbe:&\x83\x95a\xd0?\xd3\xdb\xe4R\xed\xd5\xc0?\xeb\x15\xf7bF\xb2\xd2?\xe0\xbdx\xf4.9\xdd?x0\xd0\xe7=&\xd6?\xc3`a8QV\xe6?\x85\xc3\x0b_R\xbb\xd7?\x03y$\xb5c:\xed?B_]\x13~\xa4\xe3?\x1d\x1bh\x8c3I\xe9?'    # noqa #E501
+
+
+class TestNodes(unittest.TestCase):
+
+    def setUp(self):
+        warnings.simplefilter('ignore', category=ImportWarning)
+        warnings.simplefilter('ignore', category=DeprecationWarning)
+        # ignore importlib warnings.
+        size = 200
+        half = size // 2
+        self.size = size
+        self.half = half
+        np.random.seed(10)
+        random_array = np.random.rand(size)
+        open_array = np.random.rand(size)
+        close_array = np.random.rand(size)
+        high_array = np.random.rand(size)
+        low_array = np.random.rand(size)
+        volume_array = np.random.rand(size)
+        indicator = np.zeros(size, dtype=np.int32)
+        indicator[0] = 1
+        indicator[half] = 1
+        df = cudf.DataFrame()
+        df['in'] = random_array
+        df['open'] = open_array
+        df['close'] = close_array
+        df['high'] = high_array
+        df['low'] = low_array
+        df['volume'] = volume_array
+        df['indicator'] = indicator
+        df['asset'] = 1
+        df['asset'].iloc[half:] = 2
+        index = np.array(list(reversed(range(0, size))))
+        df.index = index
+        gt_index = np.concatenate([index[1:half], index[half+1:]])
+        self._cudf_data = df
+        self.gt = pd.Series(np.frombuffer(ground_truth, dtype=np.float64),
+                            index=gt_index)
+        gt_index2 = np.concatenate([index[19:half], index[half+19:]])
+        self.gt1 = pd.Series(np.frombuffer(g_t1, dtype=np.float64),
+                             index=gt_index2)
+        self.gt2 = pd.Series(np.frombuffer(g_t2, dtype=np.float64),
+                             index=gt_index2)
+        self.gt3 = pd.Series(np.frombuffer(g_t3, dtype=np.float64),
+                             index=gt_index2)
+
+    def tearDown(self):
+        pass
+
+    @ordered
+    def test_return(self):
+        '''Test return feature node'''
+        conf = {
+        }
+        node_obj = {"id": "abc",
+                    "type": "ReturnFeatureNode",
+                    "conf": conf,
+                    "inputs": []}
+        task = Task(node_obj)
+        inN = ReturnFeatureNode(task)
+        o = inN.process([self._cudf_data])
+        err, index_err = error_function_index(o['returns'], self.gt)
+        msg = "bad error %f\n" % (err,)
+        self.assertTrue(np.isclose(err, 0, atol=1e-6), msg)
+        msg = "bad error %f\n" % (index_err,)
+        self.assertTrue(np.isclose(index_err, 0, atol=1e-6), msg)
+
+    @ordered
+    def test_indicator(self):
+        '''Test indicator node'''
+        conf = {
+            "indicators": [
+                {"function": "port_chaikin_oscillator",
+                 "columns": ["high", "low", "close", "volume"],
+                 "args": [10, 20]},
+                {"function": "port_bollinger_bands",
+                 "columns": ["close"],
+                 "args": [10],
+                 "outputs": ["b1", "b2"]}
+            ],
+            "remove_na": True
+        }
+        node_obj = {"id": "abc",
+                    "type": "IndicatorNode",
+                    "conf": conf,
+                    "inputs": []}
+        task = Task(node_obj)
+        inN = IndicatorNode(task)
+        o = inN.process([self._cudf_data])
+        err, index_err = error_function_index(o['CH_OS_10_20'], self.gt1)
+        msg = "bad error %f\n" % (err,)
+        self.assertTrue(np.isclose(err, 0, atol=1e-6), msg)
+        msg = "bad error %f\n" % (index_err,)
+        self.assertTrue(np.isclose(index_err, 0, atol=1e-6), msg)
+
+        err, index_err = error_function_index(o['BO_BA_b1_10'], self.gt2)
+        msg = "bad error %f\n" % (err,)
+        self.assertTrue(np.isclose(err, 0, atol=1e-6), msg)
+        msg = "bad error %f\n" % (index_err,)
+        self.assertTrue(np.isclose(index_err, 0, atol=1e-6), msg)
+
+        err, index_err = error_function_index(o['BO_BA_b2_10'], self.gt3)
+        msg = "bad error %f\n" % (err,)
+        self.assertTrue(np.isclose(err, 0, atol=1e-6), msg)
+        msg = "bad error %f\n" % (index_err,)
+        self.assertTrue(np.isclose(index_err, 0, atol=1e-6), msg)
+
+    @ordered
+    def test_asset_indicator(self):
+        '''Test asset indicator node'''
+        conf = {
+        }
+        node_obj = {"id": "abc",
+                    "type": "AssetIndicatorNode",
+                    "conf": conf,
+                    "inputs": []}
+        task = Task(node_obj)
+        inN = AssetIndicatorNode(task)
+
+        gt = self._cudf_data.to_pandas()['indicator']
+
+        o = inN.process([self._cudf_data.drop('indicator')])
+
+        err, index_err = error_function_index(o['indicator'], gt)
+        msg = "bad error %f\n" % (err,)
+        self.assertTrue(np.isclose(err, 0, atol=1e-6), msg)
+        msg = "bad error %f\n" % (index_err,)
+        self.assertTrue(np.isclose(index_err, 0, atol=1e-6), msg)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/unit/test_rolling.py b/tests/unit/test_rolling.py
index 1451af2f..e83ebf31 100644
--- a/tests/unit/test_rolling.py
+++ b/tests/unit/test_rolling.py
@@ -57,37 +57,43 @@ def test_rolling_functions(self):
         gpu_result = Rolling(self.average_window, self._cudf_data['in']).mean()
         cpu_result = self._pandas_data[
             'in'].rolling(self.average_window).mean()
-        err = error_function(cudf.Series(gpu_result), cpu_result)
+        err = error_function(cudf.Series(gpu_result, nan_as_null=False),
+                             cpu_result)
         msg = "bad error %f\n" % (err,)
         self.assertTrue(np.isclose(err, 0, atol=1e-6), msg)
 
         gpu_result = Rolling(self.average_window, self._cudf_data['in']).max()
         cpu_result = self._pandas_data['in'].rolling(self.average_window).max()
-        err = error_function(cudf.Series(gpu_result), cpu_result)
+        err = error_function(cudf.Series(gpu_result, nan_as_null=False),
+                             cpu_result)
         msg = "bad error %f\n" % (err,)
         self.assertTrue(np.isclose(err, 0, atol=1e-6), msg)
 
         gpu_result = Rolling(self.average_window, self._cudf_data['in']).min()
         cpu_result = self._pandas_data['in'].rolling(self.average_window).min()
-        err = error_function(cudf.Series(gpu_result), cpu_result)
+        err = error_function(cudf.Series(gpu_result, nan_as_null=False),
+                             cpu_result)
         msg = "bad error %f\n" % (err,)
         self.assertTrue(np.isclose(err, 0, atol=1e-6), msg)
 
         gpu_result = Rolling(self.average_window, self._cudf_data['in']).sum()
         cpu_result = self._pandas_data['in'].rolling(self.average_window).sum()
-        err = error_function(cudf.Series(gpu_result), cpu_result)
+        err = error_function(cudf.Series(gpu_result, nan_as_null=False),
+                             cpu_result)
         msg = "bad error %f\n" % (err,)
         self.assertTrue(np.isclose(err, 0, atol=1e-6), msg)
 
         gpu_result = Rolling(self.average_window, self._cudf_data['in']).std()
         cpu_result = self._pandas_data['in'].rolling(self.average_window).std()
-        err = error_function(cudf.Series(gpu_result), cpu_result)
+        err = error_function(cudf.Series(gpu_result, nan_as_null=False),
+                             cpu_result)
         msg = "bad error %f\n" % (err,)
         self.assertTrue(np.isclose(err, 0, atol=1e-6), msg)
 
         gpu_result = Rolling(self.average_window, self._cudf_data['in']).var()
         cpu_result = self._pandas_data['in'].rolling(self.average_window).var()
-        err = error_function(cudf.Series(gpu_result), cpu_result)
+        err = error_function(cudf.Series(gpu_result, nan_as_null=False),
+                             cpu_result)
         msg = "bad error %f\n" % (err,)
         self.assertTrue(np.isclose(err, 0, atol=1e-6), msg)
 
@@ -98,7 +104,8 @@ def test_ewm_functions(self):
         cpu_result = self._pandas_data[
             'in'].ewm(span=self.average_window,
                       min_periods=self.average_window).mean()
-        err = error_function(cudf.Series(gpu_result), cpu_result)
+        err = error_function(cudf.Series(gpu_result, nan_as_null=False),
+                             cpu_result)
         msg = "bad error %f\n" % (err,)
         self.assertTrue(np.isclose(err, 0, atol=1e-6), msg)
 
diff --git a/tests/unit/test_taskgraph_api.py b/tests/unit/test_taskgraph_api.py
index d612b74e..8e4c4726 100644
--- a/tests/unit/test_taskgraph_api.py
+++ b/tests/unit/test_taskgraph_api.py
@@ -58,7 +58,7 @@ def setUp(self):
         import cudf
 
         # warmup
-        s = cudf.Series([1, 2, 3, None, 4])
+        s = cudf.Series([1, 2, 3, None, 4], nan_as_null=False)
         del(s)
         gc.collect()
 
diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index 5e06c36f..bcdb925b 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -56,7 +56,8 @@ def test_diff_functions(self):
         for window in [-1, -2, -3, 1, 2, 3]:
             gpu_result = diff(self._cudf_data['in'], window)
             cpu_result = self._pandas_data['in'].diff(window)
-            err = error_function(cudf.Series(gpu_result), cpu_result)
+            err = error_function(cudf.Series(gpu_result, nan_as_null=False),
+                                 cpu_result)
             msg = "bad error %f\n" % (err,)
             self.assertTrue(np.isclose(err, 0, atol=1e-6), msg)
 
@@ -66,7 +67,8 @@ def test_shift_functions(self):
         for window in [-1, -2, -3, 1, 2, 3]:
             gpu_result = shift(self._cudf_data['in'], window)
             cpu_result = self._pandas_data['in'].shift(window)
-            err = error_function(cudf.Series(gpu_result), cpu_result)
+            err = error_function(cudf.Series(gpu_result, nan_as_null=False),
+                                 cpu_result)
             msg = "bad error %f\n" % (err,)
             self.assertTrue(np.isclose(err, 0, atol=1e-6), msg)
 
diff --git a/tests/unit/utils.py b/tests/unit/utils.py
index 612dbc71..be3a71d0 100644
--- a/tests/unit/utils.py
+++ b/tests/unit/utils.py
@@ -36,3 +36,26 @@ def error_function(gpu_series, result_series):
     pan_arr = pan_arr[~np.isnan(pan_arr) & ~np.isinf(pan_arr)]
     err = np.abs(gpu_arr - pan_arr).max()
     return err
+
+
+def error_function_index(gpu_series, result_series):
+    """
+    utility function to compare GPU array vs CPU array
+    Parameters
+    ------
+    gpu_series: cudf.Series
+        GPU computation result series
+    result_series: pandas.Series
+        Pandas computation result series
+
+    Returns
+    -----
+    double
+        maximum error of the two arrays
+    int
+        maximum index value diff
+    """
+    err = error_function(gpu_series, result_series)
+    error_index = np.abs(gpu_series.index.to_array() -
+                         result_series.index.values).max()
+    return err, error_index