Repository: Neuraxio/Neuraxle (language: python)

Issue:
Bug: "automl_loop_clean_kata.py" example not updated in documentation website
This example: 
https://github.com/Neuraxio/Neuraxle/blob/master/examples/auto_ml/plot_automl_loop_clean_kata.py

Should set this param to False so as to make us realize when the example is out of date to update it when updating the documentation site: 
https://github.com/Neuraxio/Neuraxle/blob/50252b6efc071c3690ee0cf9b2c188389e7a149f/neuraxle/metaopt/auto_ml.py#L732


Discussion / hints:

Fix patch:
diff --git a/README.rst b/README.rst
index d2a0607d..56b0ac7c 100644
--- a/README.rst
+++ b/README.rst
@@ -231,7 +231,7 @@ We thank these organisations for generously supporting the project:
 
     <img src="https://raw.githubusercontent.com/Neuraxio/Neuraxle/master/assets/images/solution_nexam_io.jpg" width="180px">
 
--  La Cité, LP: https://www.lacitelp.com/
+-  La Cité, LP: https://www.lacitelp.com/accueil
 
 .. raw:: html
 
diff --git a/Why Neuraxle.rst b/Why Neuraxle.rst
index f86203b1..4d303f3f 100644
--- a/Why Neuraxle.rst
+++ b/Why Neuraxle.rst
@@ -48,9 +48,8 @@ by reloading the save. The save can be dumped to a RAM disk for
 more performance and avoid truly writing to disks.
 
 Neuraxle is compatible with most other ML and DL libraries. We're
-currently already writing savers for PyTorch and TensorFlow in the
-`Neuraxle-PyTorch <https://github.com/Neuraxio/Neuraxle-PyTorch>`__
-and `Neuraxle-TensorFlow <https://github.com/Neuraxio/Neuraxle-TensorFlow>`__
+currently already writing savers for TensorFlow in the
+`Neuraxle-TensorFlow <https://github.com/Neuraxio/Neuraxle-TensorFlow>`__
 extensions of this project.
 
 Time Series Processing
@@ -65,19 +64,13 @@ series projects, as
 With the various abstractions that Neuraxle provides, it's easy to get
 started building a time-series processing project. Neuraxle is also the
 backbone of `the Neuraxio Time Series
-project <https://www.neuraxio.com/en/time-series-solution>`__, which is
+project <https://www.neuraxio.com/pages/neuraxios-time-series-solution>`__, which is
 a premium software package built on top of Neuraxle for business boost
 their time series machine learning projects by providing out-of-the-box
 specialized pipeline steps. Some of those specialized steps are featured
 in the `Deep Learning Pipelines <#deep-learning-pipelines>`__ section above.
 
-Note: `the Neuraxio Time Series
-project <https://www.neuraxio.com/en/time-series-solution>`__ is
-different from the Neuraxle project, those are separate projects.
-Neuraxio is commited to build open-source software, and defines itself
-as an open-source company. Learn more on `Neuraxle's
-license <#license>`__. The Neuraxle library is free and will always stay
-free, while Neuraxio Time Series is a premium add-on to Neuraxle.
+Note: `the Neuraxio Time Series project <https://www.neuraxio.com/pages/neuraxios-time-series-solution>`__ is different from the Neuraxle project; those are separate projects. Neuraxio is commited to build open-source software, and defines itself as an open-source company. Learn more on `Neuraxle's license <#license>`__. The Neuraxle library is free and will always stay free, while Neuraxio Time Series is a premium add-on to Neuraxle.
 
 Automatic Machine Learning
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/examples/Hyperparams And Distributions.ipynb b/examples/Hyperparams And Distributions.ipynb
index 4a15cda3..41360fae 100644
--- a/examples/Hyperparams And Distributions.ipynb
+++ b/examples/Hyperparams And Distributions.ipynb
@@ -507,7 +507,7 @@
     "        'criterion': Choice(['gini', 'entropy']), \n",
     "        'splitter': Choice(['best', 'random']),\n",
     "        'min_samples_leaf': RandInt(2, 5), \n",
-    "        'min_samples_split': RandInt(1, 3) \n",
+    "        'min_samples_split': RandInt(2, 4) \n",
     "    })\n",
     ").set_hyperparams(HyperparameterSamples({\n",
     "    'criterion': 'gini', \n",
@@ -520,9 +520,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "neuraxle_notebooks",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "neuraxle_notebooks"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -534,7 +534,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/Introduction to Automatic Hyperparameter Tuning.ipynb b/examples/Introduction to Automatic Hyperparameter Tuning.ipynb
index b6408104..a948db47 100644
--- a/examples/Introduction to Automatic Hyperparameter Tuning.ipynb
+++ b/examples/Introduction to Automatic Hyperparameter Tuning.ipynb
@@ -17,14 +17,14 @@
    "source": [
     "## AutoML loop\n",
     "\n",
-    "Neuraxle provides an AutoML step which can perform automatic tuning of the hyperparameters of any neuraxle-defined pipeline. It also provides multiple hyperparameter research strategies, interface to store and load results and a high degree of customisability. The following tutorial will provide an example of the main features of the AutoML step."
+    "Neuraxle provides an AutoML step which can perform automatic tuning of the hyperparameters of any neuraxle-defined pipeline. It also provides multiple hyperparameter research strategies, interfaces to store and load results and a high degree of customisability. The following tutorial will provide an example of the main features of the AutoML step."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 0. Define your problem\n",
+    "### Define your problem\n",
     "For this tutorial, we'll use a synthetic binary classification dataset generated by sklearn with 10k samples."
    ]
   },
@@ -70,7 +70,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 1. Define your pipeline\n",
+    "### Define your pipeline\n",
     "\n",
     "Consider a sklearn classifier with a defined hyperparameter space."
    ]
@@ -140,7 +140,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 2. Choose a validation splitter\n",
+    "### Choose a validation splitter\n",
     "\n",
     "Let's pick the [ValidationSplitter](https://www.neuraxle.org/stable/api/neuraxle.metaopt.auto_ml.html?highlight=validationsplitter#neuraxle.metaopt.auto_ml.ValidationSplitter) which is enough for most machine learning problems:"
    ]
@@ -169,7 +169,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3. Define a the main scoring metric with [ScoringCallback](https://www.neuraxle.org/stable/api/neuraxle.metaopt.callbacks.html?highlight=scoringcallback#neuraxle.metaopt.callbacks.ScoringCallback)\n",
+    "### Define a the main scoring metric with [ScoringCallback](https://www.neuraxle.org/stable/api/neuraxle.metaopt.callbacks.html?highlight=scoringcallback#neuraxle.metaopt.callbacks.ScoringCallback)\n",
     "\n",
     "Let's pick the accuracy score as our scoring metric: "
    ]
@@ -195,7 +195,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 4. Add other metric callbacks with [MetricCallback](https://www.neuraxle.org/stable/api/neuraxle.metaopt.callbacks.html?highlight=metriccallback#neuraxle.metaopt.callbacks.MetricCallback) (optional)\n",
+    "### Add other metric callbacks with [MetricCallback](https://www.neuraxle.org/stable/api/neuraxle.metaopt.callbacks.html?highlight=metriccallback#neuraxle.metaopt.callbacks.MetricCallback) (optional)\n",
     "\n",
     "During training, we'll monitor the f1 score, the precision and the recall on the training and validation dataset. We will make use of the sklearn implementation of these metric, using a weighted average since we are in a multiclass setting."
    ]
@@ -227,7 +227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 5. Select an hyperparams repository \n",
+    "### Select an hyperparams repository \n",
     "\n",
     "For the sake of this example, let's pick the [InMemoryHyperparamsRepository](https://www.neuraxle.org/stable/api/neuraxle.metaopt.auto_ml.html?highlight=inmemoryhyperparamsrepository#neuraxle.metaopt.auto_ml.InMemoryHyperparamsRepository) because we don't need to persist trial files. [HyperparamsJSONRepository](https://www.neuraxle.org/stable/api/neuraxle.metaopt.auto_ml.html?highlight=hyperparamsjsonrepository#neuraxle.metaopt.auto_ml.HyperparamsJSONRepository)​ is also available if you want to use json files to persist trials.\n"
    ]
@@ -271,7 +271,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 6. Select an hyperparams optimizer \n",
+    "### Select an hyperparams optimizer \n",
     "\n",
     "We'll use the Tree Parzen Estimator algorithm as a research strategy."
    ]
@@ -308,7 +308,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 7. Create, and launch AutoML loop\n",
+    "### Create, and launch AutoML loop\n",
     "\n",
     "We are now ready to create the AutoML step that will execute the training loop:"
    ]
@@ -358,12 +358,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[15:24:09][INFO][auto_ml][246]: \n",
+      "[12:44:31][INFO][auto_ml][246]: \n",
       "new trial: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 2.9571948437372586e-06,\n",
-      "            \"eta0\": 5.925957754860976e-06,\n",
+      "            \"alpha\": 2.486686368952862e-07,\n",
+      "            \"eta0\": 2.6464605881716034e-05,\n",
       "            \"learning_rate\": \"constant\",\n",
       "            \"loss\": \"hinge\",\n",
       "            \"max_iter\": 1,\n",
@@ -371,18 +371,18 @@
       "        }\n",
       "    }\n",
       "}\n",
-      "[15:24:09][INFO][auto_ml][838]: trial 1/10\n",
-      "[15:24:09][INFO][auto_ml][588]: fitting trial 1/10 split 1/1\n",
+      "[12:44:31][INFO][auto_ml][862]: trial 1/10\n",
+      "[12:44:31][INFO][auto_ml][589]: fitting trial 1/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 2.9571948437372586e-06,\n",
+      "            \"alpha\": 2.486686368952862e-07,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 5.925957754860976e-06,\n",
+      "            \"eta0\": 2.6464605881716034e-05,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -404,27 +404,27 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "[15:24:09][INFO][auto_ml][629]: epoch 1/10\n",
-      "[15:24:09][INFO][auto_ml][629]: epoch 2/10\n",
-      "[15:24:10][INFO][auto_ml][629]: epoch 3/10\n",
-      "[15:24:10][INFO][auto_ml][629]: epoch 4/10\n",
-      "[15:24:10][INFO][auto_ml][629]: epoch 5/10\n",
-      "[15:24:11][INFO][auto_ml][629]: epoch 6/10\n",
-      "[15:24:11][INFO][auto_ml][629]: epoch 7/10\n",
-      "[15:24:11][INFO][auto_ml][629]: epoch 8/10\n",
-      "[15:24:12][INFO][auto_ml][629]: epoch 9/10\n",
-      "[15:24:12][INFO][auto_ml][629]: epoch 10/10\n",
-      "[15:24:12][INFO][auto_ml][601]: success trial 1/10 split 1/1\n",
+      "[12:44:31][INFO][auto_ml][630]: epoch 1/10\n",
+      "[12:44:32][INFO][auto_ml][630]: epoch 2/10\n",
+      "[12:44:32][INFO][auto_ml][630]: epoch 3/10\n",
+      "[12:44:32][INFO][auto_ml][630]: epoch 4/10\n",
+      "[12:44:33][INFO][auto_ml][630]: epoch 5/10\n",
+      "[12:44:33][INFO][auto_ml][630]: epoch 6/10\n",
+      "[12:44:33][INFO][auto_ml][630]: epoch 7/10\n",
+      "[12:44:34][INFO][auto_ml][630]: epoch 8/10\n",
+      "[12:44:34][INFO][auto_ml][630]: epoch 9/10\n",
+      "[12:44:35][INFO][auto_ml][630]: epoch 10/10\n",
+      "[12:44:35][INFO][auto_ml][602]: success trial 1/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 2.9571948437372586e-06,\n",
+      "            \"alpha\": 2.486686368952862e-07,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 5.925957754860976e-06,\n",
+      "            \"eta0\": 2.6464605881716034e-05,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -446,32 +446,32 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "best score: 0.935 at epoch 9\n",
-      "[15:24:12][INFO][auto_ml][246]: \n",
+      "best score: 0.86875 at epoch 3\n",
+      "[12:44:35][INFO][auto_ml][246]: \n",
       "new trial: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 3.2005132006096184e-09,\n",
-      "            \"eta0\": 7.685094217367982e-05,\n",
+      "            \"alpha\": 4.3584204924186783e-08,\n",
+      "            \"eta0\": 7.594554486620926e-06,\n",
       "            \"learning_rate\": \"constant\",\n",
       "            \"loss\": \"hinge\",\n",
       "            \"max_iter\": 1,\n",
-      "            \"penalty\": \"l1\"\n",
+      "            \"penalty\": \"elasticnet\"\n",
       "        }\n",
       "    }\n",
       "}\n",
-      "[15:24:12][INFO][auto_ml][838]: trial 2/10\n",
-      "[15:24:12][INFO][auto_ml][588]: fitting trial 2/10 split 1/1\n",
+      "[12:44:35][INFO][auto_ml][862]: trial 2/10\n",
+      "[12:44:35][INFO][auto_ml][589]: fitting trial 2/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 3.2005132006096184e-09,\n",
+      "            \"alpha\": 4.3584204924186783e-08,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 7.685094217367982e-05,\n",
+      "            \"eta0\": 7.594554486620926e-06,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -479,7 +479,7 @@
       "            \"max_iter\": 1,\n",
       "            \"n_iter_no_change\": 5,\n",
       "            \"n_jobs\": null,\n",
-      "            \"penalty\": \"l1\",\n",
+      "            \"penalty\": \"elasticnet\",\n",
       "            \"power_t\": 0.5,\n",
       "            \"random_state\": null,\n",
       "            \"shuffle\": true,\n",
@@ -493,27 +493,27 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "[15:24:12][INFO][auto_ml][629]: epoch 1/10\n",
-      "[15:24:13][INFO][auto_ml][629]: epoch 2/10\n",
-      "[15:24:13][INFO][auto_ml][629]: epoch 3/10\n",
-      "[15:24:14][INFO][auto_ml][629]: epoch 4/10\n",
-      "[15:24:14][INFO][auto_ml][629]: epoch 5/10\n",
-      "[15:24:14][INFO][auto_ml][629]: epoch 6/10\n",
-      "[15:24:15][INFO][auto_ml][629]: epoch 7/10\n",
-      "[15:24:15][INFO][auto_ml][629]: epoch 8/10\n",
-      "[15:24:15][INFO][auto_ml][629]: epoch 9/10\n",
-      "[15:24:16][INFO][auto_ml][629]: epoch 10/10\n",
-      "[15:24:16][INFO][auto_ml][601]: success trial 2/10 split 1/1\n",
+      "[12:44:35][INFO][auto_ml][630]: epoch 1/10\n",
+      "[12:44:35][INFO][auto_ml][630]: epoch 2/10\n",
+      "[12:44:36][INFO][auto_ml][630]: epoch 3/10\n",
+      "[12:44:36][INFO][auto_ml][630]: epoch 4/10\n",
+      "[12:44:36][INFO][auto_ml][630]: epoch 5/10\n",
+      "[12:44:37][INFO][auto_ml][630]: epoch 6/10\n",
+      "[12:44:37][INFO][auto_ml][630]: epoch 7/10\n",
+      "[12:44:37][INFO][auto_ml][630]: epoch 8/10\n",
+      "[12:44:38][INFO][auto_ml][630]: epoch 9/10\n",
+      "[12:44:38][INFO][auto_ml][630]: epoch 10/10\n",
+      "[12:44:38][INFO][auto_ml][602]: success trial 2/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 3.2005132006096184e-09,\n",
+      "            \"alpha\": 4.3584204924186783e-08,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 7.685094217367982e-05,\n",
+      "            \"eta0\": 7.594554486620926e-06,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -521,7 +521,7 @@
       "            \"max_iter\": 1,\n",
       "            \"n_iter_no_change\": 5,\n",
       "            \"n_jobs\": null,\n",
-      "            \"penalty\": \"l1\",\n",
+      "            \"penalty\": \"elasticnet\",\n",
       "            \"power_t\": 0.5,\n",
       "            \"random_state\": null,\n",
       "            \"shuffle\": true,\n",
@@ -535,32 +535,32 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "best score: 0.981875 at epoch 9\n",
-      "[15:24:16][INFO][auto_ml][246]: \n",
+      "best score: 0.863125 at epoch 8\n",
+      "[12:44:38][INFO][auto_ml][246]: \n",
       "new trial: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 6.216619583755207e-05,\n",
-      "            \"eta0\": 0.0005027161239398104,\n",
+      "            \"alpha\": 4.709948725293029e-05,\n",
+      "            \"eta0\": 7.33993047202169e-06,\n",
       "            \"learning_rate\": \"constant\",\n",
       "            \"loss\": \"hinge\",\n",
       "            \"max_iter\": 1,\n",
-      "            \"penalty\": \"elasticnet\"\n",
+      "            \"penalty\": \"l1\"\n",
       "        }\n",
       "    }\n",
       "}\n",
-      "[15:24:16][INFO][auto_ml][838]: trial 3/10\n",
-      "[15:24:16][INFO][auto_ml][588]: fitting trial 3/10 split 1/1\n",
+      "[12:44:38][INFO][auto_ml][862]: trial 3/10\n",
+      "[12:44:38][INFO][auto_ml][589]: fitting trial 3/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 6.216619583755207e-05,\n",
+      "            \"alpha\": 4.709948725293029e-05,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 0.0005027161239398104,\n",
+      "            \"eta0\": 7.33993047202169e-06,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -568,7 +568,7 @@
       "            \"max_iter\": 1,\n",
       "            \"n_iter_no_change\": 5,\n",
       "            \"n_jobs\": null,\n",
-      "            \"penalty\": \"elasticnet\",\n",
+      "            \"penalty\": \"l1\",\n",
       "            \"power_t\": 0.5,\n",
       "            \"random_state\": null,\n",
       "            \"shuffle\": true,\n",
@@ -582,27 +582,27 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "[15:24:16][INFO][auto_ml][629]: epoch 1/10\n",
-      "[15:24:16][INFO][auto_ml][629]: epoch 2/10\n",
-      "[15:24:17][INFO][auto_ml][629]: epoch 3/10\n",
-      "[15:24:17][INFO][auto_ml][629]: epoch 4/10\n",
-      "[15:24:17][INFO][auto_ml][629]: epoch 5/10\n",
-      "[15:24:18][INFO][auto_ml][629]: epoch 6/10\n",
-      "[15:24:18][INFO][auto_ml][629]: epoch 7/10\n",
-      "[15:24:18][INFO][auto_ml][629]: epoch 8/10\n",
-      "[15:24:19][INFO][auto_ml][629]: epoch 9/10\n",
-      "[15:24:19][INFO][auto_ml][629]: epoch 10/10\n",
-      "[15:24:19][INFO][auto_ml][601]: success trial 3/10 split 1/1\n",
+      "[12:44:38][INFO][auto_ml][630]: epoch 1/10\n",
+      "[12:44:39][INFO][auto_ml][630]: epoch 2/10\n",
+      "[12:44:39][INFO][auto_ml][630]: epoch 3/10\n",
+      "[12:44:39][INFO][auto_ml][630]: epoch 4/10\n",
+      "[12:44:40][INFO][auto_ml][630]: epoch 5/10\n",
+      "[12:44:40][INFO][auto_ml][630]: epoch 6/10\n",
+      "[12:44:40][INFO][auto_ml][630]: epoch 7/10\n",
+      "[12:44:41][INFO][auto_ml][630]: epoch 8/10\n",
+      "[12:44:41][INFO][auto_ml][630]: epoch 9/10\n",
+      "[12:44:41][INFO][auto_ml][630]: epoch 10/10\n",
+      "[12:44:42][INFO][auto_ml][602]: success trial 3/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 6.216619583755207e-05,\n",
+      "            \"alpha\": 4.709948725293029e-05,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 0.0005027161239398104,\n",
+      "            \"eta0\": 7.33993047202169e-06,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -610,7 +610,7 @@
       "            \"max_iter\": 1,\n",
       "            \"n_iter_no_change\": 5,\n",
       "            \"n_jobs\": null,\n",
-      "            \"penalty\": \"elasticnet\",\n",
+      "            \"penalty\": \"l1\",\n",
       "            \"power_t\": 0.5,\n",
       "            \"random_state\": null,\n",
       "            \"shuffle\": true,\n",
@@ -624,38 +624,38 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "best score: 0.9875 at epoch 8\n"
+      "best score: 0.86375 at epoch 9\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[15:24:19][INFO][auto_ml][246]: \n",
+      "[12:44:42][INFO][auto_ml][246]: \n",
       "new trial: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 3.750425925408276e-05,\n",
-      "            \"eta0\": 0.00021376032579806638,\n",
+      "            \"alpha\": 2.2747983494317353e-06,\n",
+      "            \"eta0\": 3.044040225320792e-06,\n",
       "            \"learning_rate\": \"constant\",\n",
       "            \"loss\": \"hinge\",\n",
       "            \"max_iter\": 1,\n",
-      "            \"penalty\": \"l2\"\n",
+      "            \"penalty\": \"l1\"\n",
       "        }\n",
       "    }\n",
       "}\n",
-      "[15:24:19][INFO][auto_ml][838]: trial 4/10\n",
-      "[15:24:19][INFO][auto_ml][588]: fitting trial 4/10 split 1/1\n",
+      "[12:44:42][INFO][auto_ml][862]: trial 4/10\n",
+      "[12:44:42][INFO][auto_ml][589]: fitting trial 4/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 3.750425925408276e-05,\n",
+      "            \"alpha\": 2.2747983494317353e-06,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 0.00021376032579806638,\n",
+      "            \"eta0\": 3.044040225320792e-06,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -663,7 +663,7 @@
       "            \"max_iter\": 1,\n",
       "            \"n_iter_no_change\": 5,\n",
       "            \"n_jobs\": null,\n",
-      "            \"penalty\": \"l2\",\n",
+      "            \"penalty\": \"l1\",\n",
       "            \"power_t\": 0.5,\n",
       "            \"random_state\": null,\n",
       "            \"shuffle\": true,\n",
@@ -677,27 +677,27 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "[15:24:19][INFO][auto_ml][629]: epoch 1/10\n",
-      "[15:24:20][INFO][auto_ml][629]: epoch 2/10\n",
-      "[15:24:20][INFO][auto_ml][629]: epoch 3/10\n",
-      "[15:24:21][INFO][auto_ml][629]: epoch 4/10\n",
-      "[15:24:21][INFO][auto_ml][629]: epoch 5/10\n",
-      "[15:24:21][INFO][auto_ml][629]: epoch 6/10\n",
-      "[15:24:22][INFO][auto_ml][629]: epoch 7/10\n",
-      "[15:24:22][INFO][auto_ml][629]: epoch 8/10\n",
-      "[15:24:22][INFO][auto_ml][629]: epoch 9/10\n",
-      "[15:24:23][INFO][auto_ml][629]: epoch 10/10\n",
-      "[15:24:23][INFO][auto_ml][601]: success trial 4/10 split 1/1\n",
+      "[12:44:42][INFO][auto_ml][630]: epoch 1/10\n",
+      "[12:44:42][INFO][auto_ml][630]: epoch 2/10\n",
+      "[12:44:42][INFO][auto_ml][630]: epoch 3/10\n",
+      "[12:44:43][INFO][auto_ml][630]: epoch 4/10\n",
+      "[12:44:43][INFO][auto_ml][630]: epoch 5/10\n",
+      "[12:44:43][INFO][auto_ml][630]: epoch 6/10\n",
+      "[12:44:44][INFO][auto_ml][630]: epoch 7/10\n",
+      "[12:44:44][INFO][auto_ml][630]: epoch 8/10\n",
+      "[12:44:44][INFO][auto_ml][630]: epoch 9/10\n",
+      "[12:44:45][INFO][auto_ml][630]: epoch 10/10\n",
+      "[12:44:45][INFO][auto_ml][602]: success trial 4/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 3.750425925408276e-05,\n",
+      "            \"alpha\": 2.2747983494317353e-06,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 0.00021376032579806638,\n",
+      "            \"eta0\": 3.044040225320792e-06,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -705,7 +705,7 @@
       "            \"max_iter\": 1,\n",
       "            \"n_iter_no_change\": 5,\n",
       "            \"n_jobs\": null,\n",
-      "            \"penalty\": \"l2\",\n",
+      "            \"penalty\": \"l1\",\n",
       "            \"power_t\": 0.5,\n",
       "            \"random_state\": null,\n",
       "            \"shuffle\": true,\n",
@@ -719,32 +719,32 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "best score: 0.984375 at epoch 7\n",
-      "[15:24:23][INFO][auto_ml][246]: \n",
+      "best score: 0.856875 at epoch 8\n",
+      "[12:44:45][INFO][auto_ml][246]: \n",
       "new trial: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 3.572193578328492e-09,\n",
-      "            \"eta0\": 1.9363822305217954e-05,\n",
+      "            \"alpha\": 0.00021119923255226656,\n",
+      "            \"eta0\": 4.1814609647256346e-05,\n",
       "            \"learning_rate\": \"constant\",\n",
       "            \"loss\": \"hinge\",\n",
       "            \"max_iter\": 1,\n",
-      "            \"penalty\": \"elasticnet\"\n",
+      "            \"penalty\": \"l2\"\n",
       "        }\n",
       "    }\n",
       "}\n",
-      "[15:24:23][INFO][auto_ml][838]: trial 5/10\n",
-      "[15:24:23][INFO][auto_ml][588]: fitting trial 5/10 split 1/1\n",
+      "[12:44:45][INFO][auto_ml][862]: trial 5/10\n",
+      "[12:44:45][INFO][auto_ml][589]: fitting trial 5/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 3.572193578328492e-09,\n",
+      "            \"alpha\": 0.00021119923255226656,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 1.9363822305217954e-05,\n",
+      "            \"eta0\": 4.1814609647256346e-05,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -752,7 +752,7 @@
       "            \"max_iter\": 1,\n",
       "            \"n_iter_no_change\": 5,\n",
       "            \"n_jobs\": null,\n",
-      "            \"penalty\": \"elasticnet\",\n",
+      "            \"penalty\": \"l2\",\n",
       "            \"power_t\": 0.5,\n",
       "            \"random_state\": null,\n",
       "            \"shuffle\": true,\n",
@@ -766,27 +766,27 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "[15:24:23][INFO][auto_ml][629]: epoch 1/10\n",
-      "[15:24:23][INFO][auto_ml][629]: epoch 2/10\n",
-      "[15:24:24][INFO][auto_ml][629]: epoch 3/10\n",
-      "[15:24:24][INFO][auto_ml][629]: epoch 4/10\n",
-      "[15:24:24][INFO][auto_ml][629]: epoch 5/10\n",
-      "[15:24:25][INFO][auto_ml][629]: epoch 6/10\n",
-      "[15:24:25][INFO][auto_ml][629]: epoch 7/10\n",
-      "[15:24:25][INFO][auto_ml][629]: epoch 8/10\n",
-      "[15:24:26][INFO][auto_ml][629]: epoch 9/10\n",
-      "[15:24:26][INFO][auto_ml][629]: epoch 10/10\n",
-      "[15:24:26][INFO][auto_ml][601]: success trial 5/10 split 1/1\n",
+      "[12:44:45][INFO][auto_ml][630]: epoch 1/10\n",
+      "[12:44:45][INFO][auto_ml][630]: epoch 2/10\n",
+      "[12:44:46][INFO][auto_ml][630]: epoch 3/10\n",
+      "[12:44:46][INFO][auto_ml][630]: epoch 4/10\n",
+      "[12:44:46][INFO][auto_ml][630]: epoch 5/10\n",
+      "[12:44:47][INFO][auto_ml][630]: epoch 6/10\n",
+      "[12:44:47][INFO][auto_ml][630]: epoch 7/10\n",
+      "[12:44:47][INFO][auto_ml][630]: epoch 8/10\n",
+      "[12:44:48][INFO][auto_ml][630]: epoch 9/10\n",
+      "[12:44:48][INFO][auto_ml][630]: epoch 10/10\n",
+      "[12:44:48][INFO][auto_ml][602]: success trial 5/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 3.572193578328492e-09,\n",
+      "            \"alpha\": 0.00021119923255226656,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 1.9363822305217954e-05,\n",
+      "            \"eta0\": 4.1814609647256346e-05,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -794,7 +794,7 @@
       "            \"max_iter\": 1,\n",
       "            \"n_iter_no_change\": 5,\n",
       "            \"n_jobs\": null,\n",
-      "            \"penalty\": \"elasticnet\",\n",
+      "            \"penalty\": \"l2\",\n",
       "            \"power_t\": 0.5,\n",
       "            \"random_state\": null,\n",
       "            \"shuffle\": true,\n",
@@ -808,13 +808,13 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "best score: 0.9625 at epoch 9\n",
-      "[15:24:26][INFO][auto_ml][246]: \n",
+      "best score: 0.870625 at epoch 8\n",
+      "[12:44:48][INFO][auto_ml][246]: \n",
       "new trial: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 5.8602271509226276e-08,\n",
-      "            \"eta0\": 0.00012460679869877366,\n",
+      "            \"alpha\": 0.000559318975827414,\n",
+      "            \"eta0\": 3.2188344829965605e-06,\n",
       "            \"learning_rate\": \"constant\",\n",
       "            \"loss\": \"hinge\",\n",
       "            \"max_iter\": 1,\n",
@@ -822,18 +822,18 @@
       "        }\n",
       "    }\n",
       "}\n",
-      "[15:24:26][INFO][auto_ml][838]: trial 6/10\n",
-      "[15:24:26][INFO][auto_ml][588]: fitting trial 6/10 split 1/1\n",
+      "[12:44:48][INFO][auto_ml][862]: trial 6/10\n",
+      "[12:44:48][INFO][auto_ml][589]: fitting trial 6/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 5.8602271509226276e-08,\n",
+      "            \"alpha\": 0.000559318975827414,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 0.00012460679869877366,\n",
+      "            \"eta0\": 3.2188344829965605e-06,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -855,27 +855,27 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "[15:24:26][INFO][auto_ml][629]: epoch 1/10\n",
-      "[15:24:27][INFO][auto_ml][629]: epoch 2/10\n",
-      "[15:24:27][INFO][auto_ml][629]: epoch 3/10\n",
-      "[15:24:28][INFO][auto_ml][629]: epoch 4/10\n",
-      "[15:24:28][INFO][auto_ml][629]: epoch 5/10\n",
-      "[15:24:28][INFO][auto_ml][629]: epoch 6/10\n",
-      "[15:24:29][INFO][auto_ml][629]: epoch 7/10\n",
-      "[15:24:29][INFO][auto_ml][629]: epoch 8/10\n",
-      "[15:24:29][INFO][auto_ml][629]: epoch 9/10\n",
-      "[15:24:30][INFO][auto_ml][629]: epoch 10/10\n",
-      "[15:24:30][INFO][auto_ml][601]: success trial 6/10 split 1/1\n",
+      "[12:44:48][INFO][auto_ml][630]: epoch 1/10\n",
+      "[12:44:49][INFO][auto_ml][630]: epoch 2/10\n",
+      "[12:44:49][INFO][auto_ml][630]: epoch 3/10\n",
+      "[12:44:49][INFO][auto_ml][630]: epoch 4/10\n",
+      "[12:44:50][INFO][auto_ml][630]: epoch 5/10\n",
+      "[12:44:50][INFO][auto_ml][630]: epoch 6/10\n",
+      "[12:44:51][INFO][auto_ml][630]: epoch 7/10\n",
+      "[12:44:51][INFO][auto_ml][630]: epoch 8/10\n",
+      "[12:44:51][INFO][auto_ml][630]: epoch 9/10\n",
+      "[12:44:52][INFO][auto_ml][630]: epoch 10/10\n",
+      "[12:44:52][INFO][auto_ml][602]: success trial 6/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 5.8602271509226276e-08,\n",
+      "            \"alpha\": 0.000559318975827414,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 0.00012460679869877366,\n",
+      "            \"eta0\": 3.2188344829965605e-06,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -897,38 +897,38 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "best score: 0.984375 at epoch 9\n"
+      "best score: 0.8575 at epoch 9\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[15:24:30][INFO][auto_ml][246]: \n",
+      "[12:44:52][INFO][auto_ml][246]: \n",
       "new trial: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 2.3280367499151023e-09,\n",
-      "            \"eta0\": 9.748179441301912e-05,\n",
+      "            \"alpha\": 2.7048149801894626e-05,\n",
+      "            \"eta0\": 5.931390737593312e-06,\n",
       "            \"learning_rate\": \"constant\",\n",
       "            \"loss\": \"hinge\",\n",
       "            \"max_iter\": 1,\n",
-      "            \"penalty\": \"l1\"\n",
+      "            \"penalty\": \"elasticnet\"\n",
       "        }\n",
       "    }\n",
       "}\n",
-      "[15:24:30][INFO][auto_ml][838]: trial 7/10\n",
-      "[15:24:30][INFO][auto_ml][588]: fitting trial 7/10 split 1/1\n",
+      "[12:44:52][INFO][auto_ml][862]: trial 7/10\n",
+      "[12:44:52][INFO][auto_ml][589]: fitting trial 7/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 2.3280367499151023e-09,\n",
+      "            \"alpha\": 2.7048149801894626e-05,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 9.748179441301912e-05,\n",
+      "            \"eta0\": 5.931390737593312e-06,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -936,7 +936,7 @@
       "            \"max_iter\": 1,\n",
       "            \"n_iter_no_change\": 5,\n",
       "            \"n_jobs\": null,\n",
-      "            \"penalty\": \"l1\",\n",
+      "            \"penalty\": \"elasticnet\",\n",
       "            \"power_t\": 0.5,\n",
       "            \"random_state\": null,\n",
       "            \"shuffle\": true,\n",
@@ -950,27 +950,27 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "[15:24:30][INFO][auto_ml][629]: epoch 1/10\n",
-      "[15:24:30][INFO][auto_ml][629]: epoch 2/10\n",
-      "[15:24:31][INFO][auto_ml][629]: epoch 3/10\n",
-      "[15:24:31][INFO][auto_ml][629]: epoch 4/10\n",
-      "[15:24:31][INFO][auto_ml][629]: epoch 5/10\n",
-      "[15:24:32][INFO][auto_ml][629]: epoch 6/10\n",
-      "[15:24:32][INFO][auto_ml][629]: epoch 7/10\n",
-      "[15:24:32][INFO][auto_ml][629]: epoch 8/10\n",
-      "[15:24:33][INFO][auto_ml][629]: epoch 9/10\n",
-      "[15:24:33][INFO][auto_ml][629]: epoch 10/10\n",
-      "[15:24:33][INFO][auto_ml][601]: success trial 7/10 split 1/1\n",
+      "[12:44:52][INFO][auto_ml][630]: epoch 1/10\n",
+      "[12:44:52][INFO][auto_ml][630]: epoch 2/10\n",
+      "[12:44:53][INFO][auto_ml][630]: epoch 3/10\n",
+      "[12:44:53][INFO][auto_ml][630]: epoch 4/10\n",
+      "[12:44:53][INFO][auto_ml][630]: epoch 5/10\n",
+      "[12:44:54][INFO][auto_ml][630]: epoch 6/10\n",
+      "[12:44:54][INFO][auto_ml][630]: epoch 7/10\n",
+      "[12:44:54][INFO][auto_ml][630]: epoch 8/10\n",
+      "[12:44:55][INFO][auto_ml][630]: epoch 9/10\n",
+      "[12:44:55][INFO][auto_ml][630]: epoch 10/10\n",
+      "[12:44:55][INFO][auto_ml][602]: success trial 7/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 2.3280367499151023e-09,\n",
+      "            \"alpha\": 2.7048149801894626e-05,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 9.748179441301912e-05,\n",
+      "            \"eta0\": 5.931390737593312e-06,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -978,7 +978,7 @@
       "            \"max_iter\": 1,\n",
       "            \"n_iter_no_change\": 5,\n",
       "            \"n_jobs\": null,\n",
-      "            \"penalty\": \"l1\",\n",
+      "            \"penalty\": \"elasticnet\",\n",
       "            \"power_t\": 0.5,\n",
       "            \"random_state\": null,\n",
       "            \"shuffle\": true,\n",
@@ -992,13 +992,13 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "best score: 0.9825 at epoch 9\n",
-      "[15:24:33][INFO][auto_ml][246]: \n",
+      "best score: 0.86 at epoch 9\n",
+      "[12:44:55][INFO][auto_ml][246]: \n",
       "new trial: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 0.0005748640211707198,\n",
-      "            \"eta0\": 0.00026322165031578186,\n",
+      "            \"alpha\": 0.0006112208912439329,\n",
+      "            \"eta0\": 0.00015190635758770187,\n",
       "            \"learning_rate\": \"constant\",\n",
       "            \"loss\": \"hinge\",\n",
       "            \"max_iter\": 1,\n",
@@ -1006,18 +1006,18 @@
       "        }\n",
       "    }\n",
       "}\n",
-      "[15:24:33][INFO][auto_ml][838]: trial 8/10\n",
-      "[15:24:33][INFO][auto_ml][588]: fitting trial 8/10 split 1/1\n",
+      "[12:44:55][INFO][auto_ml][862]: trial 8/10\n",
+      "[12:44:55][INFO][auto_ml][589]: fitting trial 8/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 0.0005748640211707198,\n",
+      "            \"alpha\": 0.0006112208912439329,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 0.00026322165031578186,\n",
+      "            \"eta0\": 0.00015190635758770187,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -1039,27 +1039,27 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "[15:24:33][INFO][auto_ml][629]: epoch 1/10\n",
-      "[15:24:34][INFO][auto_ml][629]: epoch 2/10\n",
-      "[15:24:34][INFO][auto_ml][629]: epoch 3/10\n",
-      "[15:24:35][INFO][auto_ml][629]: epoch 4/10\n",
-      "[15:24:35][INFO][auto_ml][629]: epoch 5/10\n",
-      "[15:24:35][INFO][auto_ml][629]: epoch 6/10\n",
-      "[15:24:36][INFO][auto_ml][629]: epoch 7/10\n",
-      "[15:24:36][INFO][auto_ml][629]: epoch 8/10\n",
-      "[15:24:36][INFO][auto_ml][629]: epoch 9/10\n",
-      "[15:24:37][INFO][auto_ml][629]: epoch 10/10\n",
-      "[15:24:37][INFO][auto_ml][601]: success trial 8/10 split 1/1\n",
+      "[12:44:55][INFO][auto_ml][630]: epoch 1/10\n",
+      "[12:44:56][INFO][auto_ml][630]: epoch 2/10\n",
+      "[12:44:56][INFO][auto_ml][630]: epoch 3/10\n",
+      "[12:44:56][INFO][auto_ml][630]: epoch 4/10\n",
+      "[12:44:57][INFO][auto_ml][630]: epoch 5/10\n",
+      "[12:44:57][INFO][auto_ml][630]: epoch 6/10\n",
+      "[12:44:57][INFO][auto_ml][630]: epoch 7/10\n",
+      "[12:44:58][INFO][auto_ml][630]: epoch 8/10\n",
+      "[12:44:58][INFO][auto_ml][630]: epoch 9/10\n",
+      "[12:44:58][INFO][auto_ml][630]: epoch 10/10\n",
+      "[12:44:59][INFO][auto_ml][602]: success trial 8/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 0.0005748640211707198,\n",
+      "            \"alpha\": 0.0006112208912439329,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 0.00026322165031578186,\n",
+      "            \"eta0\": 0.00015190635758770187,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -1081,32 +1081,32 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "best score: 0.984375 at epoch 6\n",
-      "[15:24:37][INFO][auto_ml][246]: \n",
+      "best score: 0.87125 at epoch 3\n",
+      "[12:44:59][INFO][auto_ml][246]: \n",
       "new trial: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 2.580319771842709e-05,\n",
-      "            \"eta0\": 6.02090815416122e-06,\n",
+      "            \"alpha\": 8.393995178826215e-08,\n",
+      "            \"eta0\": 1.4466730901136765e-06,\n",
       "            \"learning_rate\": \"constant\",\n",
       "            \"loss\": \"hinge\",\n",
       "            \"max_iter\": 1,\n",
-      "            \"penalty\": \"elasticnet\"\n",
+      "            \"penalty\": \"l2\"\n",
       "        }\n",
       "    }\n",
       "}\n",
-      "[15:24:37][INFO][auto_ml][838]: trial 9/10\n",
-      "[15:24:37][INFO][auto_ml][588]: fitting trial 9/10 split 1/1\n",
+      "[12:44:59][INFO][auto_ml][862]: trial 9/10\n",
+      "[12:44:59][INFO][auto_ml][589]: fitting trial 9/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 2.580319771842709e-05,\n",
+      "            \"alpha\": 8.393995178826215e-08,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 6.02090815416122e-06,\n",
+      "            \"eta0\": 1.4466730901136765e-06,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -1114,7 +1114,7 @@
       "            \"max_iter\": 1,\n",
       "            \"n_iter_no_change\": 5,\n",
       "            \"n_jobs\": null,\n",
-      "            \"penalty\": \"elasticnet\",\n",
+      "            \"penalty\": \"l2\",\n",
       "            \"power_t\": 0.5,\n",
       "            \"random_state\": null,\n",
       "            \"shuffle\": true,\n",
@@ -1128,27 +1128,27 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "[15:24:37][INFO][auto_ml][629]: epoch 1/10\n",
-      "[15:24:37][INFO][auto_ml][629]: epoch 2/10\n",
-      "[15:24:38][INFO][auto_ml][629]: epoch 3/10\n",
-      "[15:24:38][INFO][auto_ml][629]: epoch 4/10\n",
-      "[15:24:38][INFO][auto_ml][629]: epoch 5/10\n",
-      "[15:24:39][INFO][auto_ml][629]: epoch 6/10\n",
-      "[15:24:39][INFO][auto_ml][629]: epoch 7/10\n",
-      "[15:24:39][INFO][auto_ml][629]: epoch 8/10\n",
-      "[15:24:40][INFO][auto_ml][629]: epoch 9/10\n",
-      "[15:24:40][INFO][auto_ml][629]: epoch 10/10\n",
-      "[15:24:41][INFO][auto_ml][601]: success trial 9/10 split 1/1\n",
+      "[12:44:59][INFO][auto_ml][630]: epoch 1/10\n",
+      "[12:44:59][INFO][auto_ml][630]: epoch 2/10\n",
+      "[12:44:59][INFO][auto_ml][630]: epoch 3/10\n",
+      "[12:45:00][INFO][auto_ml][630]: epoch 4/10\n",
+      "[12:45:00][INFO][auto_ml][630]: epoch 5/10\n",
+      "[12:45:00][INFO][auto_ml][630]: epoch 6/10\n",
+      "[12:45:01][INFO][auto_ml][630]: epoch 7/10\n",
+      "[12:45:01][INFO][auto_ml][630]: epoch 8/10\n",
+      "[12:45:01][INFO][auto_ml][630]: epoch 9/10\n",
+      "[12:45:02][INFO][auto_ml][630]: epoch 10/10\n",
+      "[12:45:02][INFO][auto_ml][602]: success trial 9/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 2.580319771842709e-05,\n",
+      "            \"alpha\": 8.393995178826215e-08,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 6.02090815416122e-06,\n",
+      "            \"eta0\": 1.4466730901136765e-06,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -1156,7 +1156,7 @@
       "            \"max_iter\": 1,\n",
       "            \"n_iter_no_change\": 5,\n",
       "            \"n_jobs\": null,\n",
-      "            \"penalty\": \"elasticnet\",\n",
+      "            \"penalty\": \"l2\",\n",
       "            \"power_t\": 0.5,\n",
       "            \"random_state\": null,\n",
       "            \"shuffle\": true,\n",
@@ -1170,38 +1170,38 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "best score: 0.935 at epoch 9\n"
+      "best score: 0.846875 at epoch 9\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[15:24:41][INFO][auto_ml][246]: \n",
+      "[12:45:02][INFO][auto_ml][246]: \n",
       "new trial: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 1.1439992912299234e-08,\n",
-      "            \"eta0\": 0.0005279082844982803,\n",
+      "            \"alpha\": 1.8668218610719405e-07,\n",
+      "            \"eta0\": 0.000714480097291864,\n",
       "            \"learning_rate\": \"constant\",\n",
       "            \"loss\": \"hinge\",\n",
       "            \"max_iter\": 1,\n",
-      "            \"penalty\": \"l1\"\n",
+      "            \"penalty\": \"elasticnet\"\n",
       "        }\n",
       "    }\n",
       "}\n",
-      "[15:24:41][INFO][auto_ml][838]: trial 10/10\n",
-      "[15:24:41][INFO][auto_ml][588]: fitting trial 10/10 split 1/1\n",
+      "[12:45:02][INFO][auto_ml][862]: trial 10/10\n",
+      "[12:45:02][INFO][auto_ml][589]: fitting trial 10/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 1.1439992912299234e-08,\n",
+      "            \"alpha\": 1.8668218610719405e-07,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 0.0005279082844982803,\n",
+      "            \"eta0\": 0.000714480097291864,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -1209,7 +1209,7 @@
       "            \"max_iter\": 1,\n",
       "            \"n_iter_no_change\": 5,\n",
       "            \"n_jobs\": null,\n",
-      "            \"penalty\": \"l1\",\n",
+      "            \"penalty\": \"elasticnet\",\n",
       "            \"power_t\": 0.5,\n",
       "            \"random_state\": null,\n",
       "            \"shuffle\": true,\n",
@@ -1223,27 +1223,27 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "[15:24:41][INFO][auto_ml][629]: epoch 1/10\n",
-      "[15:24:41][INFO][auto_ml][629]: epoch 2/10\n",
-      "[15:24:41][INFO][auto_ml][629]: epoch 3/10\n",
-      "[15:24:42][INFO][auto_ml][629]: epoch 4/10\n",
-      "[15:24:42][INFO][auto_ml][629]: epoch 5/10\n",
-      "[15:24:42][INFO][auto_ml][629]: epoch 6/10\n",
-      "[15:24:43][INFO][auto_ml][629]: epoch 7/10\n",
-      "[15:24:43][INFO][auto_ml][629]: epoch 8/10\n",
-      "[15:24:44][INFO][auto_ml][629]: epoch 9/10\n",
-      "[15:24:44][INFO][auto_ml][629]: epoch 10/10\n",
-      "[15:24:44][INFO][auto_ml][601]: success trial 10/10 split 1/1\n",
+      "[12:45:02][INFO][auto_ml][630]: epoch 1/10\n",
+      "[12:45:02][INFO][auto_ml][630]: epoch 2/10\n",
+      "[12:45:03][INFO][auto_ml][630]: epoch 3/10\n",
+      "[12:45:03][INFO][auto_ml][630]: epoch 4/10\n",
+      "[12:45:03][INFO][auto_ml][630]: epoch 5/10\n",
+      "[12:45:04][INFO][auto_ml][630]: epoch 6/10\n",
+      "[12:45:04][INFO][auto_ml][630]: epoch 7/10\n",
+      "[12:45:04][INFO][auto_ml][630]: epoch 8/10\n",
+      "[12:45:05][INFO][auto_ml][630]: epoch 9/10\n",
+      "[12:45:05][INFO][auto_ml][630]: epoch 10/10\n",
+      "[12:45:06][INFO][auto_ml][602]: success trial 10/10 split 1/1\n",
       "hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 1.1439992912299234e-08,\n",
+      "            \"alpha\": 1.8668218610719405e-07,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 0.0005279082844982803,\n",
+      "            \"eta0\": 0.000714480097291864,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -1251,7 +1251,7 @@
       "            \"max_iter\": 1,\n",
       "            \"n_iter_no_change\": 5,\n",
       "            \"n_jobs\": null,\n",
-      "            \"penalty\": \"l1\",\n",
+      "            \"penalty\": \"elasticnet\",\n",
       "            \"power_t\": 0.5,\n",
       "            \"random_state\": null,\n",
       "            \"shuffle\": true,\n",
@@ -1265,18 +1265,18 @@
       "        \"DataShuffler\": {}\n",
       "    }\n",
       "}\n",
-      "best score: 0.9875 at epoch 8\n",
-      "[15:24:44][INFO][auto_ml][807]: \n",
+      "best score: 0.875625 at epoch 9\n",
+      "[12:45:06][INFO][auto_ml][831]: \n",
       "best hyperparams: {\n",
       "    \"MiniBatchSequentialPipeline\": {\n",
       "        \"Joiner\": {},\n",
       "        \"SKLearnWrapper_SGDClassifier\": {\n",
-      "            \"alpha\": 6.216619583755207e-05,\n",
+      "            \"alpha\": 1.8668218610719405e-07,\n",
       "            \"average\": false,\n",
       "            \"class_weight\": null,\n",
       "            \"early_stopping\": false,\n",
       "            \"epsilon\": 0.1,\n",
-      "            \"eta0\": 0.0005027161239398104,\n",
+      "            \"eta0\": 0.000714480097291864,\n",
       "            \"fit_intercept\": true,\n",
       "            \"l1_ratio\": 0.15,\n",
       "            \"learning_rate\": \"constant\",\n",
@@ -1302,7 +1302,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1314,7 +1314,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1326,7 +1326,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1338,7 +1338,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1350,7 +1350,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1362,7 +1362,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABM90lEQVR4nO3dd3hUZdrH8e89Jb0BSaihKYLoWhEs64rtXcWCgh17YVcFLCBgRVkVRLBgBbsr6+ra26qri2UVEFBRQSnSMqGFkl6m3e8fc8AhBIiQyYTk/lwXV2bOOXPmnrn0/OY8zznPI6qKMcYYU5Mr3gUYY4xpnCwgjDHG1MoCwhhjTK0sIIwxxtTKAsIYY0ytLCCMMcbUygLC7LFEREVkb+fxkyJye1223YX3GSQiH+9qnY2BiJSJSNedbHO0iCxsqJpM4yd2H4SJFxH5EPhGVe+osbw/MAXooKrBHbxegW6quqQO71WnbUWkM7AM8O7ovY1pDuwMwsTTC8CFIiI1ll8ETLMD9NZExB3vGkzzYgFh4uktoBVw9OYFItICOBV4UUR6i8gMESkSkdUi8qiIJNS2IxF5XkTujnp+k/OaVSJyeY1tTxGR70SkRETyReTOqNVfOH+LnGaZI0TkUhH5X9TrjxSR2SJS7Pw9MmrdZyLyNxH5SkRKReRjEcneTs19RcQnIreIyHoRWS4ig2p8pidE5AMRKQeOFZF2IvK6iBSKyDIRGRa1vdvZ16/Oe88VkTxnXXRzXD8RWeBsUyAiI6Lridrfvs7nKRKR+SJyeo3aHhOR9539zBKRvWr7nGbPZQFh4kZVK4FXgYujFp8D/KKq84AQcAOQDRwBHA9cs7P9ishJwAjgRKAbcEKNTcqd98wCTgGuFpEznHV/cv5mqWqaqs6ose+WwPvAZCLh9gDwvoi0itrsAuAyIBdIcGrZnjbO52sPXAJMFZHuNfZ1D5AOfA28C8xztj8euF5E/uxseyNwPtAPyAAuBypqec9ngL+oajqwP/DfmhuIiNd5r4+dzzEUmFajtvOAu4AWwBKnTtOEWECYeHsBOEtEkpznFzvLUNW5qjpTVYOqupxIv8QxddjnOcBzqvqTqpYDd0avVNXPVPVHVQ2r6g/Ay3XcL0QCZbGq/t2p62XgF+C0qG2eU9VFUQF40E72ebuqVqvq50TC55yodW+r6leqGgb+AOSo6lhV9avqUuApIgdqgCuB21R1oUbMU9UNtbxfAOgpIhmquklVv61lm8OBNGC8817/Bd4jEkCbvamq3zhNgdPq8DnNHsYCwsSVqv4PWA+c4TRR9Ab+ASAi+4jIeyKyRkRKgHuJ/NremXZAftTzFdErRaSPiEx3mmmKgb/Wcb+b972ixrIVRH7Rb7Ym6nEFkQPt9mxyQix6X+2inkd/jk5AO6fJp0hEioBbgNbO+jzg151+AhhI5CxjhYh8LiJH1LJNOyDfCabo2nb1c5o9kAWEaQxeJHLmcCHwkaqudZY/QeTXeTdVzSByMKzZoV2b1UQOlpt1rLH+H8A7QJ6qZgJPRu13Z5f1rSJyoI7WESioQ121aSEiqTX2tSrqeXQ9+cAyVc2K+peuqv2i1u+0H0BVZ6tqfyJNR28ROcupaRWQJyLRx4jd+ZxmD2QBYRqDF4n0E1yF07zkSAdKgDIR6QFcXcf9vQpcKiI9RSQFGFNjfTqwUVWrRKQ3kXb+zQqBMLC9ewY+APYRkQtExCMi5wI9iTS/7Kq7RCRBRI4m0kH/r+1s9w1QKiKjRCTZ6ZTeX0QOc9Y/DfxNRLpJxAE1+kZw3meQiGSqaoDI9xtmW7OInBWMFBGviPQl0oz2z934nGYPYwFh4s7pX/gaSCXyy36zEUQO3qVE2tpfqeP+/g08RKTzdQnbdsJeA4wVkVLgDqJ+QatqBZHO1q+cZpzDa+x7A5GD+HBgAzASOFVV19eltlqsATYR+cU+Dfirqv6ync8Vct77ICL3aqwnEgqZziYPOJ/lYyIH/meA5Fp2dRGw3Gm2+yswqOYGquonEggnO+/zOHDx9mozTZPdKGdMnDi/yl9S1Q5xLsWYWtkZhDHGmFpZQBhjjKmVNTEZY4yplZ1BGGOMqZUn3gXUl+zsbO3cuXO8yzDGmD3K3Llz16tqTm3rmkxAdO7cmTlz5sS7DGOM2aOISM2RAbawJiZjjDG1soAwxhhTKwsIY4wxtbKAMMYYUysLCGOMMbWKWUCIyLMisk5EftrOehGRySKyRER+EJFDotZdIiKLnX+XxKpGY4wx2xfLM4jngZN2sP5kItNBdgMGExn7f/OUjmOAPkQmjxkjkXmKjTHGNKCY3Qehql+ISOcdbNIfeFEjY33MFJEsEWkL9AX+o6obAUTkP0SC5uVY1Hn/nTcwf//tDf1vTOORHAiQVlFNSnkVCeVVuDetJ3HFTLISUuJdmomzytRkrnnyi3rfbzxvlGvP1tMp+pxl21u+DREZTOTsg44da04aVjcBDfJRy6N26bXGNBSV2k/2vfoXMrWYzHAp6cFyMgIVpFdXkV5RTWpFFUnl1SSUlLH3/+bQY9XGBq7aNBRf2/Kdb7QL9ug7qVV1KjAVoFevXrs06uAtdz3CLfVa1Y6t9vn4++Tryft1Md0X+0n2w5pWsGjfFmzI3Zdb7n2mAatpOE/feyfhwiAeVyr+BA/lniClruotE30mqZf0oJckv6LBSqoSyhg49HradqjfqRIeuG0oUliOpwI0qAS1gugpoUXS8EgyeIRQCmhOKjfe/cgO9/nKc4+Q/9NPSGUAdwBc1SAh0DCohgkTIKxVQHUtrxZEkhAScYkHEVek4dcNoQQlnOiClESSWraltKKQUEoy/tQkKlISKU1JojQpidKEJEo8qazztmRxYmfK09O3fZe+l5BGKRnhUjJDpaQHK8msriStqoq0imqSK6rwllfjqazk5H6DOPDwPrv1PZuGtV+M9hvPgChg63mDOzjLCog0M0Uv/6zBqoqRKRNvIbzoC/b5eQOnFUK1BxZ285K/915UunO5ffyUeJdYbx4fMxpvsReXOxV/gpsyb4ByF5DpAapJCSvpQS9ZlYKGKqhKrmbgNVfWaxis9vn4x+R7cW2qxF0paChMSMtRrUQBP+CSdDySAgmpBFMhoX0uQ24bv9U+pk2+mweuvhxXNbiCCiGBsKKECevmA79/y4TWIecfuBBJxkUCLknA7UpEXBD2KuEECCW6kPQU/nTKAA478th6+9wAjz14J8WlJQTTUqhKTaIsOZGy5ERKEpMo9aZQ4kllbVIOxcmZBMW7zesfr6gm89OPyQiXkhEsJ8NfueWsJKWyCgk3jRGg1VWX6c33DAlVAcbc8Ld6329Mh/t2+iDeU9X9a1l3CjAE6EekQ3qyqvZ2OqnnApuvavoWOHRzn8T29OrVSxvbWEyrfT7+8dAQ8pb8SvclQRKC4MuFxftmU9KyE8mZ7blu9H3xLnOXrfb5eP3RySRVpSDeJKq9Lko9ASpdgS3bpIUTSQt48PpDhLSMcCb8dczd9VrH0sXzeePJh/EUB3BXCuFQiKCW8dsvdsElGbglEbwQSIPMbp258oY7tvos0x4cu4N9bOZBJAkXCYi4cYmgblCvEvJCKMlFYossTh10OV27xep3Xf2YN3MW738wjXBKKv60RCpSEilLSaI0MZGShGRKvSkUu9MpkQzKJS3e5Zod6BJcxowTz9yl14rIXFXtVdu6mJ1BiMjLRM4EskXER+TKJC+Aqj5JZPL3fkTmDK4ALnPWbRSRvwGznV2N3Vk4NDYP3TOM5F9n0/2XIvpthMoEmN8jgYK99qFtCwhIN0aPujfeZf4uq30+Xn/4IZKC6eBJojpBKPH4qU5KgSQQrSI9nESWP4GcgBAIl5PQPpnLRtxcr3X8OHcWH/79abwlQdxVQigcIqSlCH7n17sLl2TgdWeiCRBIE/IOPYhzLxu69T5eeoqHrrgMd9XmMNjePpRQshBM8tCifVtOPe+qem/2ipcDD+9T56akyRPuoNRfRiglBW0kP7ylHn7bNo1zIXD7Q7CLAbEjTWbCoHifQSxdvIA3nxhF58XL2OfXEJ4wrGgrLOmRS0ZmKmSlUeLZmyEj7olbjXX105zZfP7PV0kMpYE3mUovlHiqCUik8URUyAgnkhpw4/H78WsZLXq049y/DN3Jnn+fT/79Bt//+wO8pWGkGsLhACEtBYLOFh7cko7L5UUTIZDu4qCT+3HCyQO27GP219P57J/TSCgL4aoSQuEgIS2J2ocbt2TgdnkJJyqBdPc2+zCmKYvLGURzMenOwWQum0f3X0roVwxlSfD9H5JY3aU7f038nDXJB1CeuC9X3zAm3qXW6qc5s/ns5X+RrOmEPYlUJigl7mqCaamA4tYqMkKJ5FYl4gr48VPK3kcdzIkDz6vXOl6fNoXlM7/BUxpG/EI47CekJQhh51DuxS3pJLhbEkqCQIaLvucM2qr9fnOgzH/1XVzVQigcIKQluAg5+/DglgwS3C0JJyn+NDd9zxtU730AxjQVdgaxC76ZMZ2vXp1Al4Ur2XtZGLfC0g7C0u5t6JxRxZ8SlvFG8rH4E3vylxtua5Ca6iISBq+SrBlOGECJu4qghAHwqIuMUBLJfnAFq6mSUg4f0I9Dj+pbr3X8/fH7WffTz3jLgACEtJqwlvDbCX8iHklD3G7CyUogw8vAq6/bqk3/t0BRxA+hsN/ZR9jZIgGPpONyuwklKYEMNyddeBV/ONSuzjEm2o7OICwgfocJt11Ozoof6f5zGS3KoDgFFvZIYW2n7oxMfJ9NmsYbyX0hY3+uuKZ+295/r83NREmhdMLeJKoSoHgHYVApJfQ9/xz273VYvdbx5ITbqVhWgKdca4RBhEgybklF3C5CyUqoRSKDht22VTv/lkApV/DLdgPF5XYTSlaCmV4G/PW6Rt9JbExjYAGxG6Z/+DrzPpjCXgsL6LoicnD9tZOLpT3a0zIJLk6exUZN442UY/FmHsQlfx1R7zXszPLFC3h/ynO/9RnUCAO3usgMJZLsFycMSul7/tn1HgaTx9xIcO0mPOWCBpWQVqJatmW9SCpuScHlFoKpSrhlKsPHPcqPc2fxnzdfIlxSibs6jMuvuAKChiCkVTUCJQm3pO0wUIwxdWcBsQsm3HIhbZYvoPsvlWRUwKZ0+KVHGmvb96BvyhKOCC9gvWbwZkpfElsdysVXXl9v770jkTB4lqRQBupNcsKgmqDTgbwlDAKCKxCbZqLVPh//eGw8ro3leMqFcEgJ1XLDmVuSEU8kDKqTXaQUhSMHfucGMtUQYapRraS260lEUpxAcRFMgXDLZC4YcrOFgTH1yDqp6+jd115k+WfPs9cvazjNp4QEFndx8VX3TpQktOHklPlcru9QGMpkakp/0lv35qpLh8Ssnt/CIB11zgxK3NUE0tKA8JYO5NZVibgC1VRrKX3Oqv8wmDb5btybqre64QytJMzmG84ynBvOUgimClUJkLS2jGB4PfgV/JCw6bfrhiAJlyTiwovHlQautC13DocSXZCSwEHHnmhXEhkTZxYQwITR59Fu2UJ6LKxi7ypYnwlfHZ5BcecDISmBP1V/w2H6FWvDWTyZcia5Hf/I4POvrPc6Hh01giR/+taXlqalsc3VRP5qqqWUPgPrvwP50XGjCf66dqubxYRqwkDYueHMI+mQkE4gDSpcAVIKNxFWoDoJlz+RVLyIKw1xpaE1how48NgT7MBvzB6i2QfEhFsv5rS35hF0wcK9Pazo1pk/nnkdxf97k77Vszi0ajGrtSVPpAwgb6/j+etZF8akjmeHj2N9ehokK26tJCOUtFUYHNjveI464c8xeW+Apx8cS8W3ywn4i4HqbW4WC6a7qKwOklHmRlygorgCkJKYgHbqQM8jj6bfGYNiVp8xpuE1+4AYec+LPFJ0HNr+AM6+eAQrp91L4qcjGa6/UkArHk8ZSJfuJ3N1/7NjVsMzw8eRn15Nm+pUwtXrObDfcTENg2hPPziWym+X43eCwePKJtgik/Kwn5SMNLrbgd+YZss6qYm0s7887V5OqPqaA3QZPrJ5O6UvPQ84nWNP6l/PlW4tOhwOO/Wwem8y2p6nJo6hal7+VsEQyHbRap+uXDa08dy7YYyJLeuk3oH7776efsHp3MhyVpDLo8lnc1Dvs7j22B1Nhlc/4hEOT064ncAPBfgDRYA/Egw5WfhdLm566KmYv78xZs/R7AOiV69jKf5mFo8kncPBhw1kSAMEAzR8ODw54Xb8PxQQcILB68rBn+NCK5VBI++yS0eNMdto9gFx7En94aT+HNmA7/nM8PENFg5PjLuFwPw1NYJBkA3QukcPLrrmppi9tzFmz9bsA6KhRcKhKubh8Ojdown/sm7bM4Z1IcIeD0OnWXOSMWbHLCAaUEOEwyN/uwkWbsAf2AQE8LpyqM5xEVxXhrsyhXMm3m3NScaYOrGAaCCx7nOYfOdwZPEm/MGoYMh1EVhTSOLGbFr/+Y9bTZpjjDE7E9OAEJGTgIcBN/C0qo6vsb4T8CyQA2wELlRVn7PuPuAUZ9O/qeorsaw1lmIZDpPH3IgsKSIQ3AgEnWBwE1izhoR12fjbt2HYA1Pr7f2MMY3L6/94mkUrFnPzzfU/fXEspxx1A48BJwI+YLaIvKOqC6I2mwi8qKoviMhxwDjgIme+6kOAg4BE4DMR+bdq1LCee4hYhcPDd9yA/FocFQy5VLUWWL0W99o0JDOXc+4Ya81JxjRB940bRUGHbH7OacfCNn8gL6clsZhgIJZnEL2BJaq6FEBE/gn0B6IDoidwo/N4OvBW1PIvVDUIBEXkB+Ak4NUY1lvvYhEOD956He7lpQSDG4EQXleOEwwbcK9WvN5c2p/ch4GD/rLb72WMaRzmfTubtz5/h187tmZBZid8h58PQE54HUeWfEc33xo4sf7HOItlQLQH8qOe+4Ca03nNAwYQaYY6E0gXkVbO8jEiMglIAY5l62ABQEQGA4MBOnbsWN/175b6DocHbhuKd1k54eBGwk4wVLZ24VpTgXt1CR5XNv72CQybaM1JxjQFT0y+m2UpLn5p24YFyd0oO2ggLg2xd/BXzlg/nY4r1nLpoCG06/B/Mash3p3UI4BHReRS4AugAAip6scichjwNVAIzABCNV+sqlOBqRAZaqOhit6Z6HD448C+uzUxz6Rbh5KwrBwNbcBPOBIMbVx41oTwrF6DSjqurFyum/JsPX4CY0xDW+Xz8dy0R1jZqQ0/t+zAkv1PJixu0rWE/SoX033VGrpUhrl6WMMNhRPLgCgA8qKed3CWbaGqq4icQSAiacBAVS1y1t0D3OOs+wewKIa11pvom+B2Jxw2BwNRwVDV1oW3MIxn1XqCQEJCLh1PO4b+51xSvx/CGNMg3nnteb5Zu4IlHVrzU1pX1veONB3lhfI5cdMMuq5cwxnH9OfA4y6PS32xDIjZQDcR6UIkGM4DLojeQESygY2qGgZuJnJF0+YO7ixV3SAiBwAHAB/HsNZ6sfk+h7ZVqRx11q6Fw6TR15KwsvK3YHDn4G/rIVgWwLuqnICWRm5665DI0PufjMGnMMbE0v3jRlPQriU/57bjl5Y9qG51EAlaTXf/Eo5bN4/2BesZFYMrknZFzAJCVYMiMgT4iMhlrs+q6nwRGQvMUdV3gL7AOBFRIk1M1zov9wJfighACZHLX4M136Mx2d1weO6RuymdsdQJBsXrzqa6nQe/uEjwVRMIFyKSjmTlMGzKczH6FMaY+rZs8QL+/u4/WNaxNfOzOrLy8PMAaBVeT5+yeXQrWMshme0YeEH9T0K2u2y473rwzIjx5Kftejg8fMcN6KJCQlocOWNo56Vb7yNZ+e7n+P0bAMGb0JK9Bx5vczMYswd45rEJLEoIsKhtG+an7E2JZCIaomtoOfttzKfTyrVcdv61tGsEl6HbcN8xtLvhcP+IwXh8pYS1EldWDsOmPMuDf7mcX9/4ENVSPK4cgnnJDJvweIw+gTGmPowbP4qVnVrzc8v2LN73OELiIUXLIh3Ma9fQaVMVQ4ffFe8yfxc7g9gNuxsOE4ddiWtdMaphyMkinOQmocBPMLwel2SgWUnc+KRdnWRMY7PK5+P1V55ifQIszmvD/PQurHW1AaBdqID9SpazV/5aTu19Ar2O+FOcq90xO4OIgd0NhweuvhzZuAkkgVC7Fngqg7jy1xJE8Cbm0vP8Uznh5Pq/8cUYs32bD/wlIT+VqYlUpCZRmpxESVISJQkplHjSKHKlUyxZBA85EwCv+tkn8CtHF/5Eh/xCRjeSDub6YAGxC3Y3HB668jK0dAMuSSfUKQsJ+tFNRbgkjVCnLIbd91iMKjemedr2wJ9MaXLiDg/80VK0jKxwMRmhMvb255NRvYj0qiqyiivYLzGLQZc3zYEwLSB+p90Nh8mXXEaoqhC3tCTxwEgHVdW8ZYRxEcpLZ4SFgzF1tsrn481XnqYoVE1VaiLlzoG/NCmJYufAX+xKp1gyCWznwJ+pJWQGS7c68KeVV5NSUUlipZ99OnZrlFcYNQQLiN9hd8Jhtc/Hv0bfQSBQiMeVQ4dTD6djl334evLzhLUcWmUzwu5rMKZWH779L75Z8j0bcjIpyGrB6uSWFLsynAP/Gdtsn6LlZGoxmcFSuvrzyax54K/ws1deV8650MYs2xELiDranXCY/fV0Zjz6dwKhdXjduRwx5CIOO/JYHrngcoK6AU9KDtc9bp3RxkBk+Oqf1ixnvRMG+cm5rErvQuiQbkDkV3/74Gq6VtU88FeRWFFtB/56ZAFRB7sTDq889whrPp5LMLwerzeXs8dHhuB++NLLCYbWkeDJZehzFg6meXr1pSn8XOijMDeTgswWrExqzeo2BxNuG7moJlVL6Rgo4PiSWbTZUESLDSVccuFQ2nX4Y5wrbx4sIHZid8Lh0btHE/xpFSHdiDcpl2EvRILggasvRyvX43Fl0/dG+6Vjmodnn5jESn8x63IiYZCf2JrV7Xqh7SODPKdrMR0Dq/hD8XLabCgma0MJF180lHYdjo5z5c2XBcQO7E44TBx1Le4VmwhrGe6MHIY9FQmHicMHIxuLcUkaKYd24g+H1hwB3Zg935RHxrFKq1iXk4kvowX5SW1Y2/1YVFwAZOkm8vyrOLBsKW3WF9FiUxkXXjSUdh2OiXPlJpoFxHY8M+K+XQ6HCTcMxrO6mLAGkJbZXP9EJByemjgGd0EZYSDUPp2rRuxZd1UaU5vHH7qLNR5lbXYmBRktWJnYlnX7n7xlfYvwBvICqzm0dDGt1xfTqqic4TeP38EeTWNhAVGLSDhU0q4qlSN/ZzhMuvYK3OuLQFxo6xYMn/wMAD/OnUXF3BWEtQxaZjNi0pQYVW9M7DwyaQxrk4R12Vn4MlqwMqEt6w/sv2V9q3AhHf2r6V36C20Ki2hV7ueGkffEsWKzOywgatidcHjwL5dB0SZckkyoQwYjJv4WAp89MIVgeD2elFyue8I6pc2eYZXPx3MvP8ayTm2Y36Ijy6LuJcgJr6Nj9WqOLF1A6/VFZFeEuO6mv8WxWlPfLCCiPHNTpM9hV8Lh4csuI1yxHrdkIftkc8PYB7daFwwWkuC2K5ZM4/fpR2/z2ZLvWNyhDT+m7cWGXucC0CW4jFPWf06HNRtpHXJxzfVj4lypiTULCMczN40nPzXS5/B7wmG1z8e/br6DoL8Qj7Qi46huXDb0tykBJ11zOVRswCOtOHzIRbEq35jd8sikMaxokcSCNu1YkNSNqp5nkKDV9PAv5sS135G3aqP1GzRDFhBsHQ5/vuIUOnfrWafXLV08n/fHTIrcAOfKoefFp201wN7Em/6KbCjBJakkHNiew448NlYfwZjf7d7xo1nZuTXzW3ZgycGno+ImU4voVT6ffQrWcFBart1w1szFNCBE5CTgYSIzyj2tquNrrO9EZJrRHGAjkZnjfM66CcApgAv4D3CdxmBs8sdG3MT61LTfHQ4fvDWNJa98QiAcaTo65a7hdO2235b1zz1yN+78UsKECbZP4+qb763v0o35XebM+IJ3v/mEJR3b8FNGV9b2icxslhfK588bv6brirVcdPoFdOnWN76FmkYjZgHhzCv9GHAi4ANmi8g7qrogarOJwIuq+oKIHAeMAy4SkSOBo4jMRQ3wP+AY4LP6rvPaiffzxI2j+fPVZ9c5HJ575G5KvlpMUDfgTczlrHsjd0dv9uPcWZR8vYSwliAts7lp0lP1XbYxdfLMYxNYmBDgl3ZtmZ/cjfIDBuDWIN0Di5vk8NSmfsXyDKI3sERVlwKIyD+B/kB0QPQEbnQeTwfech4rkAQkAEJkjuq1sSr06gfq3rb64K3Xwa8bCGsx7tRchj27bafz5iuWvEk5DHvC5o82DWv8uFHkd8xhQXYHFjkzm6VpKftXLqbHqtXs4/dyxbUj412m2QPEMiDaA/lRz31AzduG5wEDiDRDnQmki0grVZ0hItOB1UQC4lFV/TmGtdbJxOF/wVVQgjrTg14/ZdtweOjyywkFI4PynX3P2DhUaZqbZYsX8OK7/2BZpzb8mNmZgsPPB6BteBXHFn3D3vmrOfPo0znwuMvjXKnZ08S7k3oE8KiIXAp8ARQAIRHZG9gX2Nxu8x8ROVpVv4x+sYgMBgYDdOzYMaaFRqYHLUJVIacVNzz6zDbbTBpyBZSvx+NqxcFXnL1Vs5Mx9enVl6bwXdk6FrZvy0+pe1Ny8Fm4NMTewaWcWfhfOq5Yy82jrenI7J5YBkQBkBf1vIOzbAtVXUXkDAIRSQMGqmqRiFwFzFTVMmfdv4EjgC9rvH4qMBUic1LH6HNsNT1ouF06Nz349DbbTBp5DbK+BCQVV89cjj7+5Fr2ZMyuu3/caHztW7Igtz2/tDuYgCSQrBXsV7WIHmtW02VTNdcOvzPeZZomJJYBMRvoJiJdiATDecAF0RuISDawUVXDwM1ErmgCWAlcJSLjiDQxHQM8FMNatyt6etBw5yxuGr/tjG/TpjyAa2UJYQ0RateCG2+/Pw6Vmqbk04/e5uufvtkyJ8KKlNbkHx656ig7vI6jSr5lH98ajul2MMf3uzS+xZomK2YBoapBERkCfETkMtdnVXW+iIwF5qjqO0BfYJyIKJEmpmudl78GHAf8SKTD+kNVfTdWtW5PzelBa7tUdeni+Wz47EfCWowrK4fhD05t6DLNHu7Tj95mxo+zKMzN2jJBToG3HcFDzgYgVcvoGPBx2obP6LxiLZddcC3tOvxfnKs2zYHE4NaCuOjVq5fOmTOnXva12ufjtdF34A9EboBrf+rhDBxU+w1Dky+8nEBgHd7EXIa9aMNomB379KO3+frHWWzIycLXIisSBu72BMULRGZL6xgooGP5etpsKKLlxhIuHjSUdtafZWJEROaqaq/a1sW7k7rRiZ4eNMGdy+HO9KC1eeiKywkFnCuW7rUrlszWvpz+IZ/N/YKN2Vn4WkbCwOdtT/DQc4DIvMkdgz6OLfqGthuKabGh2GZLM42KBUSUmtODnjV+7HavRJo49AqkbANuaUnPi061K5aauS+nf8jns79gQ24mvpZZ+JJz8bnbE4gKg7xgAX2LZtNuYxEt1m8Og6PiXLkx22cB4YieHtSTnMuw57ffXDRp9LW4CktAknF1z95q/CXT9M2Z8QUffflv1udmUbD5zMDdnsBhkTBI1grygj7+VDyHdhuKaLm+mPMGXkKXbhYGZs9iAQFMHHk17pXFW6YHve6p7YfDK889gnt5CSENEm6dyY13PdCAlZqGNmfGF3z0v3+zPieLVS2yyE/JId/dgcBhkSGwk7SCjsGCSBhsLKZFYTHnD7yYLt2OjHPlxuy+Zh8QE0f8BZdvE2ENQtT0oLVZ7fOx9uNvCekmXJm5DJ+87f0QpmkYN34UP+/TkZlZ+1PSa3MYVDpnBnMjzUQWBqaJa/YB0a33kawo+Ax/rosRk7e9OzraazffQSBciDchl2FT7YqlpubL6R/y/uK5fNOpKwv6nI9oiD/4f+aAVV/TsrCY8wdcTJduR8S7TGMajF3mWkcPXnU54ZJ1eF25nH3/9juvzZ5n/LhR/LJPHjNb7EeRtKBFeANHbJxPjyUFjLSRTk0TZ5e57qaJw65ESjbilpZ0OvMYC4cmYM6ML3h93ufM7rwX8/tEmpD28//CgOX/Y8CBx9Dr+GFxrtCY+LOA2IkHbhuKa10JSCK6Vwv6n3NJvEsyu2HCuFH8snd7ZrTcj03dTyNLN/HnjV/RY7HP5kUwpgYLiB14+9UXcC0pIqR+tHUrRtzzcLxLMrtg3rezeXXWv5ndpSs/9jkHFTc9/T/Tf8UMTunWi6PPGhrvEo1plCwgtmO1z8eKNz+PXLGUkcMNO+nANo3PpHGj+Xmvtsxo1ZMNPU4nQ4s5sWgm+y5aaUNhG1MHFhDb8a+b7yAQXhe5YmkH90WYxmXZ4gU88/GrzO3alR/6nEVIPPQILOSUpd/w5477c/yAa3e+E2MMYAFRqwevupyw37liaZyNsbQneHDCrfzcMZsZOftR2PMM0rSUvsXfsP/CFXa2YMwusoCo4f7rrsBVshG3tKD9qX3siqVGbJXPx5Q3n2LOXl35vteZhMRDt8BiTlo+l+Oz9+akM6+Jd4nG7NEsIKI8fMcNuNeWgSQQ6pK53SG+TXw9fP/t/NyhBTNye7J2/zNJ1TKOKZnNfguXc+soO1swpr5YQDg+eGsaumgDqtWRK5bGPRrvkkyUVT4fU16fyrd7d+G7Q08nKF72Cv7KCSs/5MjEbAZecHW8SzSmybGAIHLF0pJXPyWkG3Gn53C9XbHUaDz+0F38lJvGjNb7svqAAaRoOUeVzuWAhSu4deS4eJdnTJMW04AQkZOAh4lMOfq0qo6vsb4TkXmoc4CNwIWq6hORY4EHozbtAZynqm/Fos5/3XIHgdA6vN5chj1tVyzF25fTP+Q/82cyr0tHvj3gZAKSQJfgMs7P/4he4RQGXW73LRjTEGIWECLiBh4DTgR8wGwReUdVF0RtNhF4UVVfEJHjgHHARao6HTjI2U9LYAnwcSzqvP+GK3FVF+Jx5XDqmOGxeAtTB09MvpulKS5+aduW+cn7ULHfGSRpJYeXfc8Bi5Zz+4ht5wM3xsRWLM8gegNLVHUpgIj8E+gPRAdET+BG5/F04K1a9nMW8G9VrYhFkTc9+DQPXnUZ2b3+QNdu+8XiLcx2jB83ivyOOczPzmPx/icREg/pWsyBFT/To2AN+3sz7WzBmDiKZUC0B/KjnvuAPjW2mQcMINIMdSaQLiKtVHVD1DbnAbXOyiMig4HBAB07dtzlQm946rldfq2pu2WLF/D3d/7B0k6t+SmrM77DzwegbXgVxxfNYq8VazjjmNM58Lgr41ypMQbi30k9AnhURC4FvgAKgNDmlSLSFvgD8FFtL1bVqcBUiAz3Hetize/3zmvPM6twJQvbt+GntL0pOuQsREPsHVzKGYX/peOKddwyevzOd2SMaXCxDIgCIC/qeQdn2RaquorIGQQikgYMVNWiqE3OAd5U1UAM6zT17KEJt7MyN5UFrdvzc8seVLc6iEStomf1IvZd8w1dNlYydPhd8S7TGLMTdQ4IEWkPdIp+jap+sYOXzAa6iUgXIsFwHnBBjX1mAxtVNQzcTOSKpmjnO8tNI3fv+NGs6Nya+S3z+LXXGai4aaEb6V02j+75a+jduhOnn3VpvMs0xvwOdQoIEbkPOJdIB/PmJiAl0ixUK1UNisgQIs1DbuBZVZ0vImOBOar6DtAXGCcim/e1ZSQ1EelM5Azk89/5mUwDmPftbN788h2W5LXlx8yurO1zHgB5oZWctPFruq5YwxXnXk27DsfFuVJjzK6q05SjIrIQOEBVq2Nf0q6J9ZSjBl6Y+iA/U8Ev7dowP6UbpZKBW4PsE1jCfoU+8vILGWWT7hizR6mPKUeXAl6g0QaEiY37x40mv30rFuS2Z+HeRxGQBFK0jP0rF9Nj1Wr2qhL+MtRaAY1piuoaEBXA9yLyKVEhoao2cW8Ts8rn47l/PMayzm34sUUnVhweaTrKDq/j6JK57J2/hhP3P4KjT7kszpUaYwAevvtmqgJljLrrkXrfd10D4h3nn2linph8NwXuIKtzW7IiPZtlCXmUH3YuAF2Cyzht3Wd0Wr6WywddS7sO/xfnao1pPp568G9sLCvE41U84ieJKtLCFWSGS2gZKiEnXEyubuI6qWCua5+Y1FCngHCGwkgANlex0C493fPMmfEFH375IevatCC/ZUuWJ7dl9R9OBUA0TNvwag4sX0inDRtov7aYEaNseAtj6tuLUx5i9bqlzoE/QBKVpIXLyQyX0ipUQk64iFwt4ipxBo8I/fbaavWwTlpQKFksc7flO3c3Sl1plLgyODQGtdb1Kqa+wAvAckCAPBG5ZCeXuZo4m3jfLWzISqEgpwXL03JZ4cmj+rBzAEjXEjr78zm0ZDHt1m2ik6RwxbUj41yxMXuul59/lJX5i3B7wnhdQZKkkrRQOVmhUlqGNx/4N3GxVEZeEHXgr1IvhZLFOsliqbsd37q7U+pKpcKVgl8TCAXchCvDHH7EyfQ99ZStbjCLpbpexTQXuEBVFzrP9wFeVtVYhNYuae5XMb3z2vN8l7+ENW1asjKzFcsT27HBlQOAW4PkhXx0rlhD3vpNtCws4pILh9LOZsszZodW+3y89a9nKKvYiMsdxOsKkKxVpIbLyQyV0SpcTLbT1JOx+cAfpUq9rJMsCiWLDa5MitzplLjTqJRk/OEEQkEP4coQ7dp04+Kh18XhE9bPVUzezeEAoKqLRMRbL9WZ322Vz8cLLz3Cxpws8rNbsCKlNStb7k+o1UEAtAqvp3N1AX8q/pE2azaxf+tODLzAxjcyZrPZM77gy8/eIaR+3O4gCVSTQiUZoTKynAN/jhaRrcVcLU5relSjeqUmsE6yWC+ZLHW3Y667B6XuVCokmUA4kVDQjVaESJAUzrt6GIfuoT/G6hoQc0TkaeAl5/kgoPn+XG9gzzw2gRVawarcFqzMaMWyhDxK+0QGukvQKjoH8zm+aBbtCzeRvbGM4Tfb2EameXr5+Ufx+Zbg8oRwu/wkaTVpWk5GqIwWodItB/5DKeUw2bb1ZKOmsV6yKHRl8r2nG8WuNMrcqVRpEkH1Egq40ApBipSWnbO5ctQdcfiUDaeuAXE1kbucN1/W+iXweEwqaubmfTub9z55g8LWLVjZsiUrktuwat8TUHEBkZFP969YQt7GjeSs28TJR51EryPOjnPVxjSMyfffSiBcRAYlpIfKyAqXRq7o0ciB/3ypimwY/O01AXVTSCaFrizWulvyi7szJVva95MIhj0Eq6G8CtxJLfGqmwx/Ikl+cAWqCUkZPY86mBMHnhefDx1HdeqD2BPs6X0Q90wYzacHHshSbyeqJAWAVC2ji38lnUrX03bdRtoFhGuuHxPnSo1pGC88OZH1RcvJkGI6BlbTM7icPAq3rC/VZNZLJoWSyUZ3JkXOr/0KSSagCYSDbkKVintDAi3Tcqj2uijzBKlw+bfsIznsJT3oJTGgaLCSKk8Zpw6+nM7desbjI8fFLvdBiMirqnqOiPxIZOylrajqAfVUY7M25pEx/L3XaXgI0rvsRzps2ET22iIuvXAI7Tr8Md7lGRNz/377X8xf8CWprlLaB9fQI7CCi3QVLqcZKJ8cFng686H3CEo0k4zU9lw19GbSgS7Af997i43//ZpE0kn2JCIJUOKuJuAJQWuo0CrSw0lkBLy08gshLSeUoVw99s44furGb4dnECLSVlVXO3NHb0NVV8Ssst9pTz2DuPH5+3il4/G0Dq/l3Nlf2FhGpsmb991sPv7gJZI95bQJraN7YCXdwytJkMh1n4WawXx3F5YltGeTtMBNFtc59+TM/eozvn73fRKqvXgklWBCApXeECWuasJOmLjVRUYokZSAC1fAT4BScnp2ZuCVf43bZ27MdnQGUdfLXFOBSlUNO5e49iAyDWijuVluTwuIVT4ft818gw9a/Yke/oWcv2ipjWlkmpzVPh8vPT+RhIRKckOFdPPn0zO8ghSJjNhTosnMd3fhV28HCt2t8FckIGvDtEzKQVwJqNtNwC343WGqXSEqxY/Kb/tPVA8ZwQQS/UCwkmp3Gcecdw779zosPh94D1QfATEXOBpoAXxFZK4Hv6oOqs9Cd8eeFBCffvQ2k4MbmJVyCEeUz2GIN4fj/9w/3mUZs9seuGcELk85LXUDe/kL2C+0jCwpByL3BCxwdWaxN4+1nhyqqpNgYwaJqenOwT+A1riyyKMuksNeEsNuEkKCOxRGQgFC4WqqU/wMvGYobffQS0gbi/q4D0JUtUJErgAeV9UJIvJ9vVXYjDz+0F280nNfFqYcwqmFnzP2yIF2w5rZIz3+wB1UBjaQpUV0CRSwX3AZN0oRBCCoLhZJHv9NOIRV3tZUBlIpLvXgScoAP+B3Dv5pXtxhIcvvpVXIHTn4q5+AVJGQncIpF19uARBHdQ4IETmCyP0PVzjL3LEpqekaP24Ur/Q+hnWSy4XL/s3Ey61JyezY/z59j5++n0tZSQnhcIAwgIDLpc5VI057i9t55BJENfJEQATQMLjE2Trs/HW2QZ1Viujm5YpLne1EERTRMCKKR0O0D6xl3+AKrmHtljqX0pY53h7kJ7ShLJhBRYmbVG9rEgORX/7JoTCtQgFC1Xbw35PUNSCuJzL155vOrHBdgek7e5GInAQ8TCRMnlbV8TXWdyIyzWgOsBG4UFV9zrqOwNNEZpVToJ+qLq9jvY3O2Em38VKffgRxc+VP73LXMJuTubGbN3MWn3z2EolSgcs5aLr47Z9bw7gJRf5qCBdhPBrCTeR55HEIj4bxEIr80yAeQng19Nsygng0hJfflnmJbPdHCfNHiBzMd/STLFTjbwwV0IoF7s58ktCbYs0kWOwhuzqXsFYTliqyslMYNORKO/g3ATG7D0JE3MAi4ETAR6Tf4nxVXRC1zb+A95zRYo8DLlPVi5x1nwH3qOp/RCQNCKtqxfberzH3Qdwy5W5e6nYiGVrC+XM+4dZRdqVSY/bkpLsIBvM5rnIWPVm5w22r1UMQN0HcBHAeS+3Pg7gJiDty6BcXQdyExE1QXIScxyFchLY8j/wN4yIsLhQhLIKqoM5zdXpsw+IClcgyARDCGvmLgjpnGkrkhkvCCuJClcjPLwUVgbBzZrIlaCKvDIcj+3G73Fx41XA7+Dchu3MfxEOqer2IvEvt90GcvoOX9waWqOpSZ1//BPoTmdd6s57Ajc7j6cBbzrY9AY+q/sd5n7Id1dmYXTttEm9060fn0ArO+W4ON1g4NFr3jRlKbvIGBlb+jxwpZhEdeDT5bMorUxCUcFhwq5CQnEr7vM5063YgBx7eh8R4F25MjOysienvzt+Ju7Dv9kB+1HMf0KfGNvOAAUSaoc4E0kWkFZF5J4pE5A0i98F8AoxW1a1OoEVkMDAYoGPHjrtQYuzM+3Y241fOYXq74zmo6kcu31DBOSPviXdZphYT7h7GPvIr1zGLpKoAX7gP4MWEAzih78UMObzmf7LGNB87DAhVnes8nINzHwRsaT6qjx9OI4BHReRS4AuggMjJrYfIZbUHAyuBV4BLgWdq1DcVmAqRJqZ6qKdeTHv2EV5s24J5mUdwXPEMRnXsxYEn23XZjcn/Pn2Pb+Z8QJ/qeYwM/0SlJvBe4pEsDXdi5G2P8Kd4F2hMI1DXTupPgROAzU09ycDHwJE7eE0BbDWvRQdn2RaquorIGQROP8NAVS0SER/wfVTz1FvA4dQIiMbowQm38srBh7HCncfAVZ/w2KAR8S7JRHnwrpEkJq/n/ypmcCOrWKMteCb5VAorWjH6lsnxLs+YRqWuAZEU3Q+gqmUizohy2zcb6CYiXYgEw3nABdEbiEg2sNE5M7mZyBVNm1+bJSI5qloIHMceMLz4PfeN4uVeJ1Ai6Vy66APG/fX2eJdkHPfdOZR2SWu5NPwVLSrK+FG6MDn5XFKSu3Ll0NHxLs+YRqmuAVEuIoeo6rcAInIosO30SVFUNSgiQ4CPiFyg96xziexYYI6qvgP0BcaJiBJpYrrWeW1IREYAn4qIAHOBp37/x2s4dzwyhpcOOw0vQa767l1uH279DY3B/fcOo2d4ITfqbNyVYf7rOYR53v04oe+FDLP+BWN2qK5DbRwG/BNYReSK7DbAuVF9FHEXz8tcb3xuPK90OoHW4bWcM/sLRtuAe3H1weuvsPDXz/hj5VwO04WUaRLvJR7JykAeI8dYM5Ix0XZ7qA1VnS0iPYDuzqKFjWmgvnhZ5fNx+4w3eL/zSfQILOTcXxZztYVD3Nx/+3VkpBVxcuVX9KOQfHKYknIGxaVZjLzl4XiXZ8wep04B4fQ33Ah0UtWrRKSbiHRX1fdiW17j9elHb/NIYD0zs//E4eVzGerN5vhht8W7rGZpwp3XkZeYz19cX5NRWclc2YfXk08gr+0h/OWiwfEuz5g9Vl37IJ4j0g9whPO8APgX0CwD4onJd/NKj278knoop6z/gr8dMcAG3Gtg82bO4j/T/84BwZ8ZrnMJV7n4j7cX8109uOmWyRwa7wKNaQLqGhB7qeq5InI+gDOyq+zsRU3R+HGjePWwP7HW1ZpByz9k0mV2BUxDenHKQ2wq/pm+ld8wQpdSpKm8nHQivup2jL7tYfrFu0BjmpC6BoRfRJJxhtsQkb2A6phV1UiNnXQrL/U5mSBerpj/LmOH2oB7DWX87cPITtvIKRVf0VY2spS2PJ4ykIryloy4eVdu9DfG7ExdA2IM8CGQJyLTgKOI3NncbNzy5N946eDTSNdSLpn7IbeOHL/zF5ndNuFvw+jqXs4w10xSKqv52t2TlxNPpnevflxz/KnxLs+YJm2nASEiLiIzyQ0gcjezANep6voY19ZoDJk2kdf36UfHUD5nf/sNI0ZZOMTSap+Pf7w0jkP9PzIyPI/qoIcPE/qwSPfmptsm7/D2fWNM/dlpQDjzUI9U1VeB9xugpkZj3rezuW/lHP7b7gQOrPqJi1dvYpAzebqpf089Mo5QxUJOrJzJcFazXjN4Pvlk1lTlMvpWu3/BmIZW1yamT5w7m18ByjcvVNWNMamqEXj1pSk82yqF7zOP4NjiGYy2Afdi5v57rqOrLOWC6lmkSjXfyd5MTj6XYEUWN46eEO/yjGm26hoQ5xLpoL6mxvKu9VtO4/DghFt59eBeLHd3YsDqT3n8guHxLqnJ+d/0D5n7zZscUfUdN+lCqtTLRwm9WSR7k7o+mWFj7IZDY+KtrgHRk0g4/JFIUHwJPBmrouLp3vtG849ex1EimVy2+APu/Yvd/FafHhx3E1nio1/l1/xRilhJDk8ln8768hbkbsjl7KsH0Llbz3iXaYyh7gHxAlACbG4IvsBZdk4sioqXMZPH8PfDTsVDiCu/e5c7ht8d75KahEin833sF/yFIYG5eCXEF+4DmJZ4CoXrE+lc1Y5Mz0aueMDC2JjGpK4Bsb+qRv+smy4iC7a79R5oxLPjeHn/08jVdZw753NG29Sgu+3Fpx+idOMPHF/xDcPJp1hTeC3pWFYG21MZaklaeSJdguUMvv+meJdqjKlFXQPiWxE5XFVnAohIH/aA+RnqIjLg3uu83+Vk9gks4rwFP3ONhcNumXD39XR0reCM6plkSAUL6MSjyWdRsT6DAC0JESavIpHcQ9ty2nmXxLtcY8x21DUgDgW+FpGVzvOOwEIR+RFQVT0gJtU1gOenPcpHhw2kT8W3DPO04vjrx8S7pD3SvO9m8+nHz9Kn+gdGhn/Cr24+8fZigXsfgvlhklt2oiK1kuxAIlK9jism2vdsTGNX14A4KaZVxNEto8ajE27msguutQH3dsHk+28lJbyCkypncCPrWa0teTb5FEo0j+yUVmQVQEFOBRCkQ7FwyvALaGvfszF7hDpNGLTLOxc5CXiYyIxyT6vq+BrrOxGZZjQH2AhcqKo+Z10I+NHZdKWqnr6j94rnhEHN0cRxw+geXsyJ/tkkSYCZrn2ZkXQQfzz6AjrkdeW9SU+zKT2BCpefdpUpuDIruPKWO+NdtjGmht2eMGgX39QNPAacCPiA2SLyjqpGd25PBF5U1RdE5DhgHHCRs65SVQ+KVX3m93vvtZdYtvRz/lQ5hxG6lDJN4t3Eo1gW7szI2x7mcODRUSOY7W7NukzIDLloU1zN4AfvjHfpxphdELOAAHoDS1R1KYCI/BPoD0QHRE8iExEBTAfeimE9Zhfdf/f1tHMXcHLV15wqZSyhHU8kn0lCeneuuOZmAL765CN++fe3FKVlAJXklSZy0IDDOfSovnGt3Riz62IZEO2B/KjnPqDmLPHziAwC+DBwJpAuIq1UdQOQJCJzgCAwXlXfqvkGIjIYGAzQsWPHev8AzdmyJT/zxuuPcoj/J4YH56EB+K/nEH7w7ssFF97M1VH9CFNuuJnytExK0qtp7U8lFF7HFZPuj2P1xpj6EMuAqIsRwKMicinwBZGZ6kLOuk6qWiAiXYH/isiPqvpr9ItVdSowFSJ9EA1XdtP16t+fZNOqGfxf5UyGs4ZCzeCl5D9TGOrA8FsncWLUtlPvuQNK01mdmUhKOEz7YuWqB+2eBmOailgGRAGQF/W8g7NsC1VdReQMAhFJAwaqapGzrsD5u1REPgMOBrYKCFO/7r/nek4LfEIP8vlWuvFuUl+69TiBi/ufvdV2q30+PnjgOTakJ+BPrKBDeQopnb1cMPaWOFVujImFWAbEbKCbiHQhEgznERmiYwsRyQY2qmoYuJnIFU2ISAugQlWrnW2OAmxYzxiadN+1XOV/E0GZlHIhw0c9xiG1bPfYiJsIJ+awISNEy2AymRUlXPmA3dNgTFMUs4BQ1aCIDAE+InKZ67OqOl9ExgJzVPUdoC8wTkSUSBPTtc7L9wWmiEgYcBHpg2hSQ3s0Fqt9Pt6bdjPXV7zLYunAO54TGTnqwW22e+ulZ9gwbz0bUtPwUE3HEg/HXX2aDaxnTBMW0/sgGpLdB/H7vfz8o7TwvcVJwdl86DmMTR3O4PxLh2yz3dTrb6c4I4VyVzVtq1IJJW7gmrtsVj1jmoK43AdhGrfN/Q3d1MdTKadz6qBx29zh/Oito/CEs1mT5SY9DO2Kgwy2Tmhjmg0LiGZo0vhIfwPAQymDGD7qsa3W/zRnNt/842OK09MIU0FeWRIdj+jMiQPPi0e5xpg4sYBoRlb7fLw/bTTXV7633f6GJ24chT+lJZsyAmQHUsAG1jOm2bKAaCZefPohWq95jyuDs/nQG+lvGBnV3zD1njuQknTWpieTpEHyioV+NrCeMc2aBUQzMOHuGzg9+J9a+xuWL17Ap0+8EbmnIamc9pUpeHL9XDbWzhqMae4sIJq4SeOvZXCg9v6G35qTgrQKpuCuKOGqB+6MU6XGmMbGAqKJiu5vWCR5vOc9nptGPQTU0pxU4qbfjdacZIzZmgVEE7R1f0NvNnXoz02XDtlBc9Lt8S7ZGNMIWUA0MRPuvoH+wY/ZWwu26m+w5iRjzO9lAdGEPDD+Gv4SeBNFtvQ3WHOSMWZXWUA0AdH9DQud/oazBwzmmRvvtuYkY8wus4DYw0X3N/zb25uiDv1J+yGft19415qTjDG7xQJiD7Z1f0N/ija1IuvnSmtOMsbUCwuIPdTm/oYwLh5KGUSLNW0JZsAqKqw5yRhTLywg9jCrfT4+eGkU11e+z0LJ4x2OJam0PSszK53mpPVcZRP4GGPqgQXEHuTFpx+izep3uSI0hw+8ffix7A9UJqajLmtOMsbUP1csdy4iJ4nIQhFZIiKja1nfSUQ+FZEfROQzEelQY32GiPhE5NFY1rknuP/u6+nje4rjg3OZmtyfGcUHUZWYQfvKFFpnKFc8cLuFgzGmXsXsDEJE3MBjwImAD5gtIu/UmDp0IvCiqr4gIscB44CLotb/jchUpM3aA+OvYfDm/obkCymtzCY3MQV3qTUnGWNiJ5ZNTL2BJaq6FEBE/gn0B6IDoidwo/N4OvDW5hUicijQGvgQqHU6vKYuur/hF6e/gYrW5JWFrTnJGBNzsQyI9kB+1HMf0KfGNvOAAcDDwJlAuoi0AjYBk4ALgRO29wYiMhgYDNCxY8d6K7wxqNnfMKtsXzqE29vVScaYBhPvTuoRwKMicimRpqQCIARcA3ygqj4R2e6LVXUqMBWgV69eGvNqG8j9d19P/+B/2EsLmJrSn/Vr82hNgKseGBnv0owxzUgsA6IAyIt63sFZtoWqriJyBoGIpAEDVbVIRI4AjhaRa4A0IEFEylR1m47upmbSfdcyOPAGIVw8lDyIrLVtuOzGy6w5yRjT4GIZELOBbiLShUgwnAdcEL2BiGQDG1U1DNwMPAugqoOitrkU6NXUw2HezFnM+WwyN1S+zy/Skfc4lhxPDpc9cFu8SzPGNFMxCwhVDYrIEOAjwA08q6rzRWQsMEdV3wH6AuNERIk0MV0bq3oauzmfTeaKqvf4wNuHecX7cPP9D8e7JGNMMyeqTaPpvlevXjpnzpx4l7FLJt13LTdUTOPf3j50OGEsBx5esy/fGGNiQ0TmqmqtV4rG9EY5s3P3/W0YV1a8wS+Sx6LQgRYOxphGwwIijiaOGc6ZoU8J4+JdjuWGMRPiXZIxxmxhAREn82bO4gDP9+ylBTybfCaj7pwc75KMMWYrFhBxMvvzR/i/0ByeTTmd4aOb/VBTxphGyAIiDiaNv5bLK97jQ09veh/TbC/cMsY0chYQDWzCXcO4ovJNFkkHfgpYp7QxpvGygGhAE8eM4DSdDsDb7uMYcdfEOFdkjDHbZwHRQObNnMV+3nl013yeST6TUbdbp7QxpnGzgGggsz5/jJOD3/BsyqkMH/1YvMsxxpidsoBoAJPGD+GKinf42N2Lw44ZGu9yjDGmTiwgYmzCnddxWeWb/Crt+SF4kHVKG2P2GBYQMfTgXSM5lel4CPOW63hG3DUp3iUZY0ydWUDEyLyZs9jHPY8eupKnU85k5B3WKW2M2bNYQMTIzM8f55TgTJ5LPoUbRz0e73KMMeZ3s4CIgc2d0p+4D6X7YefHuxxjjNklFhD17L47h3Fp5Vssk7Z8FzyQPx5/arxLMsaYXRLTgBCRk0RkoYgsEZFtpgwVkU4i8qmI/CAin4lIh6jl34rI9yIyX0T+Gss668vD995CP/kCL0HedJ/ATXc9GO+SjDFml8UsIETEDTwGnAz0BM4XkZ41NpsIvKiqBwBjgXHO8tXAEap6ENAHGC0i7WJVa32YN3MWXfU7/qDLeDplACPtTmljzB4ulmcQvYElqrpUVf3AP4H+NbbpCfzXeTx983pV9atqtbM8McZ11ouvP3+C0wJf81xSP+uUNsY0CbE88LYH8qOe+5xl0eYBA5zHZwLpItIKQETyROQHZx/3qeqqmm8gIoNFZI6IzCksLKz3D1BXE8cN4cqKd5juPohuhw2KWx3GGFOf4v3LfARwjIh8BxwDFAAhAFXNd5qe9gYuEZHWNV+sqlNVtZeq9srJyWnIurcYf+d1XFL1Nisll9l+65Q2xjQdsQyIAiAv6nkHZ9kWqrpKVQeo6sHArc6yoprbAD8BR8ew1l3yyPjbOVm+JIkAr3v+j5F/s34HY0zTEcuAmA10E5EuIpIAnAe8E72BiGSLyOYabgaedZZ3EJFk53EL4I/AwhjW+rvNmzmLvNC3HKi/8kzKmYy8zcLBGNO0xCwgVDUIDAE+An4GXlXV+SIyVkROdzbrCywUkUVAa+AeZ/m+wCwRmQd8DkxU1R9jVeuu+OqLKZwR+B8vJJ3M9aOeiHc5xhhT7zyx3LmqfgB8UGPZHVGPXwNeq+V1/wEOiGVtu2Pi+KFcV/kWn7sPZK/DLox3OcYYExPx7qTe49x351AuqXybAslmRvBg65Q2xjRZFhC/w5OT7uL/5GtSqOJVz8mMHvtwvEsyxpiYsYD4HXKr53CwLmFq6gBG3mbhYIxp2iwg6uixCVcwwP8Ff0/6MzeMfDLe5RhjTMxZQNTBxHuHcFX5m3zhOoBW3QbGuxxjjGkQFhA7Mf7OYVxU/S6rpRUzggfTb+C58S7JGGMahAXEDjz9yHhOlBmkU8mrnpMZNdZuhjPGNB8WEDuQVfo1h+oinko5k5vsTmljTDNjAbEdj953JWf5P2da4v9x3agp8S7HGGManAVELSaOG8ZVFW/ylWt/WuxzVrzLMcaYuLCAqGH8ncMYVPUu66QFXwYPtU5pY0yzZQER5cUpD3G8zCSLMv7p7cdo65Q2xjRjFhBRkjZ8xmG6kKdSBnDTrRYOxpjmzQLC8ciEqzjHP52XE09gmHVKG2OMBQTA/fcO46ryN5nh6om3w5/jXY4xxjQKzT4gxt8xjAv877FeMvk82IuzLhoc75KMMaZRiGlAiMhJIrJQRJaIyOha1ncSkU9F5AcR+UxEOjjLDxKRGSIy31kX00uJFrnzeDmhH6PHPhLLtzHGmD2KqGpsdiziBhYBJwI+InNUn6+qC6K2+Rfwnqq+ICLHAZep6kUisg+gqrpYRNoBc4F9VbVoe+/Xq1cvnTNnTkw+izHGNFUiMldVe9W2LpZnEL2BJaq6VFX9wD+B/jW26Qn813k8ffN6VV2kqoudx6uAdUBODGs1xhhTQywDoj2QH/Xc5yyLNg8Y4Dw+E0gXkVbRG4hIbyAB+DVGdRpjjKlFvDupRwDHiMh3wDFAARDavFJE2gJ/J9L0FK75YhEZLCJzRGROYWFhQ9VsjDHNQiwDogDIi3rewVm2haquUtUBqnowcKuzrAhARDKA94FbVXVmbW+gqlNVtZeq9srJsRYoY4ypT7EMiNlANxHpIiIJwHnAO9EbiEi2iGyu4WbgWWd5AvAm8KKqvhbDGo0xxmxHzAJCVYPAEOAj4GfgVVWdLyJjReR0Z7O+wEIRWQS0Bu5xlp8D/Am4VES+d/4dFKtajTHGbCtml7k2NLvM1Rhjfr94XeZqjDFmD9ZkziBEpBBYsRu7yAbW11M5ezr7LrZm38fW7Pv4TVP4Ljqpaq1X+TSZgNhdIjJne6dZzY19F1uz72Nr9n38pql/F9bEZIwxplYWEMYYY2plAfGbqfEuoBGx72Jr9n1szb6P3zTp78L6IIwxxtTKziCMMcbUygLCGGNMrZp9QOxs1rvmRETyRGS6iCxwZvO7Lt41xZuIuEXkOxF5L961xJuIZInIayLyi4j8LCJHxLumeBKRG5z/T34SkZdFJCneNdW3Zh0Qzqx3jwEnE5m86HwR6RnfquIqCAxX1Z7A4cC1zfz7ALiOyFhiBh4GPlTVHsCBNOPvRUTaA8OAXqq6P+AmMiBpk9KsA4K6zXrXbKjqalX91nlcSuQAUHOSp2bDmSP9FODpeNcSbyKSSWQAzWcAVNW/oymAmwkPkCwiHiAFWBXneupdcw+Iusx61yyJSGfgYGBWnEuJp4eAkcA2k1U1Q12AQuA5p8ntaRFJjXdR8aKqBcBEYCWwGihW1Y/jW1X9a+4BYWohImnA68D1qloS73riQUROBdap6tx419JIeIBDgCecCb7KgWbbZyciLYi0NnQB2gGpInJhfKuqf809IHY6611zIyJeIuEwTVXfiHc9cXQUcLqILCfS9HiciLwU35Liygf4VHXzGeVrRAKjuToBWKaqhaoaAN4AjoxzTfWuuQfETme9a05ERIi0Mf+sqg/Eu554UtWbVbWDqnYm8t/Ff1W1yf1CrCtVXQPki0h3Z9HxwII4lhRvK4HDRSTF+f/meJpgp70n3gXEk6oGRWTzrHdu4FlVnR/nsuLpKOAi4EcR+d5ZdouqfhC/kkwjMhSY5vyYWgpcFud64kZVZ4nIa8C3RK7++44mOOyGDbVhjDGmVs29ickYY8x2WEAYY4yplQWEMcaYWllAGGOMqZUFhDHGmFpZQBjTCIhIXxsx1jQ2FhDGGGNqZQFhzO8gIheKyDci8r2ITHHmiygTkQeduQE+FZEcZ9uDRGSmiPwgIm864/cgInuLyCciMk9EvhWRvZzdp0XNtzDNuUPXmLixgDCmjkRkX+Bc4ChVPQgIAYOAVGCOqu4HfA6McV7yIjBKVQ8AfoxaPg14TFUPJDJ+z2pn+cHA9UTmJulK5M52Y+KmWQ+1YczvdDxwKDDb+XGfDKwjMhz4K842LwFvOPMnZKnq587yF4B/iUg60F5V3wRQ1SoAZ3/fqKrPef490Bn4X8w/lTHbYQFhTN0J8IKq3rzVQpHba2y3q+PXVEc9DmH/f5o4syYmY+ruU+AsEckFEJGWItKJyP9HZznbXAD8T1WLgU0icrSz/CLgc2emPp+InOHsI1FEUhryQxhTV/YLxZg6UtUFInIb8LGIuIAAcC2RyXN6O+vWEemnALgEeNIJgOjRTy8CpojIWGcfZzfgxzCmzmw0V2N2k4iUqWpavOswpr5ZE5Mxxpha2RmEMcaYWtkZhDHGmFpZQBhjjKmVBYQxxphaWUAYY4yplQWEMcaYWv0/tmmNPJGbuvsAAAAASUVORK5CYII=\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAB7J0lEQVR4nOydd3hURduH7zlla7KbHhKS0EGKoAgq2Av2gmLvvVfU1/LZ+2uv2HvvHV+7WLCgKCK9t/Rks5vte86Z749dMCLdhCRwbq+9NrtnzswzkczvzDzPPCOklNjY2NjY2KyM0t4G2NjY2Nh0TGyBsLGxsbFZJbZA2NjY2NisElsgbGxsbGxWiS0QNjY2NjarxBYIGxsbG5tVYguETadFCCGFEL0zPz8qhLhmXcpuQDvHCiE+3VA7OwJCiLAQoudayuwkhJi1sWyy6fgIex+ETXshhPgf8LOU8tqVvj8YeAwok1Iaa7hfAn2klHPXoa11KiuE6A4sAPQ1tW1jszlgzyBs2pPngOOEEGKl748HXrIH6L8jhFDb2wabzQtbIGzak3eBfGCn5V8IIXKBA4DnhRDbCiF+EEI0CSGqhBAPCSEcq6pICPGsEOLmFp8vy9xTKYQ4ZaWy+wshfhNChIQQS4QQ17e4/E3mvSmzLDNCCHGSEOK7FvePFEJMEkIEM+8jW1z7WghxkxDieyFEsxDiUyFEwWps3lUIsVQIcZUQol4IsVAIcexKfXpECDFeCBEBdhNClAoh3hJC1AkhFgghLmhRXs3UNS/T9q9CiPLMtZbLcfsJIaZnyiwTQlza0p4W9fXP9KdJCDFNCHHQSrY9LIT4KFPPT0KIXqvqp03nxRYIm3ZDShkDXgdOaPH1EcBMKeUUwAQuBgqAEcAewDlrq1cIsQ9wKTAK6APsuVKRSKbNHGB/4GwhxOjMtZ0z7zlSyiwp5Q8r1Z0HfAQ8QFrc7gE+EkLktyh2DHAyUAQ4Mrasji6Z/nUFTgQeF0L0W6muW4BsYCLwATAlU34P4CIhxN6ZsmOBo4H9AB9wChBdRZtPAWdKKbOBQcCXKxcQQuiZtj7N9ON84KWVbDsKuAHIBeZm7LTZhLAFwqa9eQ44TAjhynw+IfMdUspfpZQ/SikNKeVC0n6JXdahziOAZ6SUf0opI8D1LS9KKb+WUk6VUlpSyj+AV9axXkgLyhwp5QsZu14BZgIHtijzjJRydgsB3GotdV4jpUxIKSeQFp8jWlx7T0r5vZTSArYECqWUN0opk1LK+cATpAdqgNOAq6WUs2SaKVLKhlW0lwIGCCF8UsqAlHLyKspsD2QBt2fa+hL4kLQALecdKeXPmaXAl9ahnzadDFsgbNoVKeV3QD0wOrNEsS3wMoAQoq8Q4kMhRLUQIgTcSvppe22UAktafF7U8qIQYjshxFeZZZogcNY61ru87kUrfbeI9BP9cqpb/BwlPdCujkBGxFrWVdric8t+dANKM0s+TUKIJuAqoDhzvRyYt9YewBjSs4xFQogJQogRqyhTCizJCFNL2za0nzadEFsgbDoCz5OeORwHfCKlrMl8/wjpp/M+Ukof6cFwZYf2qqgiPVgup2Kl6y8D7wPlUko/8GiLetcW1ldJeqBuSQWwbB3sWhW5QgjvSnVVtvjc0p4lwAIpZU6LV7aUcr8W19fqB5BSTpJSHkx66ehd0rOclakEyoUQLceIf9NPm06ILRA2HYHnSfsJTiezvJQhGwgBYSHEFsDZ61jf68BJQogBQggPcN1K17OBRillXAixLel1/uXUARawuj0D44G+QohjhBCaEOJIYADp5ZcN5QYhhEMIsRNpB/0bqyn3M9AshLhcCOHOOKUHCSGGZ64/CdwkhOgj0gxeyTdCpp1jhRB+KWWK9O/X4p/8RHpW8B8hhC6E2JX0Mtqr/6KfNp0MWyBs2p2Mf2Ei4CX9ZL+cS0kP3s2k19pfW8f6PgbuI+18ncs/nbDnADcKIZqBa2nxBC2ljJJ2tn6fWcbZfqW6G0gP4pcADcB/gAOklPXrYtsqqAYCpJ/YXwLOklLOXE2/zEzbW5Heq1FPWhT8mSL3ZPryKemB/ynAvYqqjgcWZpbtzgKOXbmAlDJJWhD2zbQzDjhhdbbZbJrYG+VsbNqJzFP5i1LKsnY2xcZmldgzCBsbGxubVWILhI2NjY3NKrGXmGxsbGxsVok9g7CxsbGxWSVaexvQWhQUFMju3bu3txk2NjY2nYpff/21XkpZuKprm4xAdO/enV9++aW9zbCxsbHpVAghVs4MsAJ7icnGxsbGZpXYAmFjY2Njs0psgbCxsbGxWSW2QNjY2NjYrBJbIGxsbGxsVoktEDY2NjY2q8QWCBsbGxubVbLJ7IOwsbGx2dyY9utPPD5tAu54itvP+L9Wr98WCBsbG5tOxs/ffcnzCyfzZcnWNJbvxbDY723Sji0QNjY2Np2Ebz//mFdqp/Nll6E0dd2TXsY8Dpv5PWOPOb9N2rMFwsbGxqaD8/n7b/FmdDFfFg0lVLIHfVOzOXLWBC4+7kJyRo1ps3ZtgbCxsbHpoHz81uu8Y1TyVeFQmrN70T85k2NnfcV1F1wPe7V9+7ZA2KygvrqKiye+zdzsUg7883euuviG9jbJxmaz5N2Xn+YDvZmv87chIvoyKDGdXWbN4poLb4C9N54dtkDYADDrz9+5bNFkfs7dAZeM8vCQA5n7xgNc3n9n+g3aqr3Ns7HZLHjthcf52J1gQpdtiAkPgxN/ssvM2fzfRTfCPhvfHlsgbPjhy8+4NlnLVM9QDqibwL4JD8/laYwv2JlpVYs47NN3uGysPZuwsWkrXnjyAT7LU/mm61Diws3W8T/YZeZcrri4fYRhOZvMkaPDhg2T9nkQ688Hr77AHXlu5mo9OXzZFzxw/GUrrl3xxC281Wsn4rjYv/Z7rhpxMBXderajtTY2mxZPPXoXXxZ7+NY/lBQOton/wU4zF3D5RnwgE0L8KqUctsprtkBsvjzz2L2M69WLSqWUY+d/yh2nXbXKMq9WFDLFNYjeqbkcNn06F110bTtYa2Oz6fDYw//l664+vvNtg4HK8NgUdpq5mMsuuX6j22ILhM0/ePD+m3hi0LY0CT8nzvyUm85Z/aDfFAhw47uP8W63nTBQOXjZt9x0wGnk5OZuRIttbDo/4x64ma+7FzIxaygWCttFf2OnOcsYe/F17WaTLRA2f+OOe67j6a12IYWDk6Z+lo6MWAceeeA2Xu/XgxmOLeifnMmRsxZw1gVXtrG1Njadn/vvu4lve3XhB+/WAIwI/8ZO86u58KJr2tkyWyBsWnDLfdfy9OBR6Bic/PvX673W2RQIcM2HT/Je153QMDlk0bdcM/pMezZhY7MK7rn3Br7tU8ZPnq1QsBgZnsyuC+s454Kr29u0FdgCYQPAtQ/fwHP998Yvg5w69ed/9fRy7z3X8+aWWzJP68VW8akcs7SBE06/qPWMtbHpxNx1z3V8068bk9xD0DDZMfQruy4Lcea5l7e3af/AFggbLn/iFl7stTclVhVnz53FqWdd+q/rXLxoPrf+8B4fFu2Imxhj5n7bJhklbWw6C/+95zq+3aIHv7oGo5Nkp+Bkdq+JtsrfW1thC8RmzoXP38HrZXvS01zAZXVBRh9zSqvWf8c91/Hm4KEsVruxbfQ3Tmw0GHP86a3aho1NR+b2e69lwha9+c01GJeMsXPTZEY1mhx/2gXtbdpasQViM6UpEOCyT5/jg6JdGZSYznVaLjvtuW+btDXrz9/578xv+F/eDvgIcvjM79YYGWVjsylwy33XMGGLfvzhHIRHRti5cTL7xpwcefwZ7W3aOmMLxGZIUyDAuV+/whc5Ixke/Y27um29UVJm3Hrvdby55bZUql3ZsflnTk3lsO+YI9q8XRubtqYpEODpZ+5naa6LJfm5LPSUsEStwCvD7NrwKwemslt9dr4xsAViM2P+nNlcMutbfvBuwy6hH3l4u4Mp6FKy0dr/+bsvub9uJl/6tydf1nP4tInp7JM2Np2It154gt+bq6gszGFRdgELHOVERDYALhmlR2oxfQNVHKAXc+CYY9rZ2g3HFojNiJ+/+5JrmiuZ4hrEfvXfcM+ex7dbCOoN91/Hm4N2oF4UsFvoJ8739WbE7qPaxRYbmzUxf85sXv/gJaoKslicl8dCdwlVSikAQlqUWpV0j1VR0RCgpDHCUWNO3GTSztgCsZnwwVsvc2e2xmy9L2MqP+fhY9s/cuLz99/iMer5Nns7SqxKDpv6YzozpY1NO/LUo3cxV8RZVpjLwqwiFmnlJIQLgGwZpEdyCd2C9ZTWNjGstG+nniGsDVsgNgNeePIBHurWjaVqV45e+Bl3ndKxdjhf+/CNvNF/R4L42atxIpf12JaB22zX3mbZbAZM/nEiH/34CVVFPhbn5LPA2ZUGpRAAVRpUmEvoFq2hvD5AWXOKE088f7Pa+NluAiGE2Ae4H1CBJ6WUt690vRvwNFAINALHSSmXZq6dCCzfbnizlPK5NbW1OQvEuAdu5rGBw2gUeZww6xNuObv9t++vindffppn/PCTZygV5mIO/eMXrhjbcWcTTYEAb732FD0r+rDbfge3tzk260BTIMBzzz3E0myNJfm5LPIWs1gtxxTpkw3yrXq6J5ZREWygtDbI3tvswrY77t7OVrcv7SIQQggVmA2MApYCk4CjpZTTW5R5A/hQSvmcEGJ34GQp5fFCiDzgF2AYIIFfgW2klIHVtbe5CsSdd1/P01vtREK4OHHqp1y3jnmV2pMrH7uZt/rsRBQP+9V+x9XtnEZ88o8T+XziJzRmO2jweanN8lHjyKNaLSIuPAhpUSjrKEnW0SXaRFFTM/nBGAfusr89C2pnPn7rdX6snE5lUQ6L/AUsdJQTEn4AHDJOd2MJ3cO1lNYH6JXSOb0D7mRub9pLIEYA10sp9858vhJASnlbizLTgH2klEuEEAIISil9QoijgV2llGdmyj0GfC2lfGV17W2OAnHLfdfyzOBRKJic8vtXXNGJDvV54ckHeLlrDr+5BtPLmM+YP6e2eUbLl558iLmROgI5Xup8WdS6/dToBdSJAqRQV5TLsxooNuooijdREA4Td+hUe3OochRQoxSveBpVpUGxVUNJsp4ukSCFgWaKwikOG3PCJuPA7Eh8Nf49vps9mfq8LJbl5rDAXUKlUrLi/10Xq4rusUoqAo2U1jdzxAHH0bNP33a2uuPTXgJxGOnB/7TM5+OB7aSU57Uo8zLwk5TyfiHEocBbQAFwMuCSUt6cKXcNEJNS3rVSG2cAZwBUVFRss2jRojbpS0fkuodu4LkBe5Elw5w65XsuHnt9e5u03jQFAtz8zqO83X0nDHQOqvqW6/c49l+F5M6fM5t3P3yFerdCQ04Wdd5saly51KhFhDMhigC6TFJs1VCcbKQwGqIwGCY3FGPbXkPY86Axq61/2q8/8eGE8dT7nNTl+qjy5FDlKKROFCKFsqLuEquaLol6ujQHKQyEKU0Kjj3h3M1qbXtDaQoEePWFR1miG9TmZbMsO5dlzmJqRdGK37FXhumeWkK3UB1d65rYKrvE3r2/gXRkgSgFHgJ6AN8AY4BBwGmsg0C0ZHOaQVzx+M282Htviq0azpo1rdNPmx97+L+83ruCaY7+bJGcxRGz5qw12+UHr77AlKo5NOZ4qPdlUevxU6vn/e0JH8Anm+hi1FEUD1AYCZPfFKYwITjkkGNb9Sn/288/5tupP9KQ66Han021J5dKrYiAkr+ijEtGKTFrKIk30KU5SEEgTDfVy5gjT91shWParz/xwYTx1Oa6qcnxs8ybx1Kt9G9iXmjVUpaspjTcRHFjiK5xbLFtRTrsEtNK5bOAmVLKMnuJafVc/Ox/ebViT3qai7igur5TbelfE02BANd98ATvlu2MgsUhi7/hqn1O5LXXnqJat2jI8VKXlU2NK4darZAm8dfgoEqDIllLcbKBoliIglAzuU0xBuSXtftT5bsvP80fdYtpyPVS48+myp1HpdaFZuFbUSZLNlNiVFMSb6Q4GKIgEGaL3K6bzP/b5bz6zDhmhGupLfBR6cuh0llIpVKyQtB1maSrWUlZvI4uwSCFDc0MLeu3SYeYdgTaSyA00k7qPYBlpJ3Ux0gpp7UoUwA0SiktIcQtgCmlvDbjpP4VGJopOpm0k7pxde1tDgJx1qv38m7xbgxIzuAa07NJRtbcf9+NvDmgP3P0PmgyhSH0Fdc8MkyJWUtRopGicDP5TRHyoikO3OuQjZJGpDV5/on7mBdvoj4vmxqfjypXHpVqCTHhWVEmRwYoSdVQEgtQ3NSMJ5bAkTJwGhK/5qZ7116M2H1Uh3uSrq+u4vlXHqcmS6Mmz88yby5L9S5/m03lyABdU9V0jTRS3BikKJhkzEHH2D6DdqA9w1z3A+4jHeb6tJTyFiHEjcAvUsr3M8tQt5GOVPoGOFdKmcjcewqw/JDkW6SUz6yprU1ZIJoCAS746mU+zd2BYbHf+W9x/006eqa+uoqbPn2R+uwsCpqbyWuK0sOdw0GHndjhBsPWpCkQ4JUXHmWZnqIuN5vqbD9VznyqlBJSwrHKe4Q08RDDLaN4ZByXFcdjJnCbSVxGEreRwpVMvxxJA2fCwGVYeIROl/xiRuy8579aalvuOK7Lz6LK76fSXcBStXTFpjNFmpRYVXRN1FHa3ERRQ4jeDr99dkgHwt4o14lZvGg+F//xBd9nDWfH5p+5Z8i/+4O26XwsXjSfd955kTAGCV0l4dBIOHTiTp24phHTdWKag5jqJK44iSouYsKdeXnWWr9LRvHIGG4Zw23FcVtpgXEvF5hUCmcyhSuRQkhJfU42lVk5/3Ace2SYMqOK0mgDJU1BCgMR9hy+62a/z6CjYwtEJ2XyjxP5v8BcfnMNZp/G77hv92M36Sdom9Zn8aL5TPrmC5Y2VBOTKeKaQsKhkXTqxBwacV0nruvENJ2Y6ky/FCcx4SYq3MTwYLUIAV5OkVVD12QNpeEAxQ3NdE3YjuPOypoEQlvVlzbtz+fvv8UtzhQzXIM5pPpLHjl6bHubZNMJqejWk4rjN3zG2RQI8Mu3XzJ34WxCRhxDwJYVtuN4c8EWiA7Iq8+M4/6yEhapfTh20SfcfVLnDmO16bzk5Oay50Fj2LO9DbFpF2yB6GA8+sBtPDZwCPWigJNmf8ytZ615P0Br8uX/PuDPl9+BlEQIgdTAcoLlVvCUFLPv0afSrbvt/7Cx6Ug8ff8tRBoaOf/Gu1u9blsgOhD33HM9Tw3ZkYjwcOqf47l+Ix2y88OEz5n01MsYyWakjCGEF2klwDQgAYQgUlPNm79fiBAeFJwoQgVFgAqWU2K6NXxlpYw+4UwKu5RuFLttbDY3pv/xO/97+hG0QAqSYMgoUobRM9lpWxtbINqZr8a/x9fzfmNZcS4TttoNgDOm/I+rLm77LKe//Pg1Ex95HiMRRsooqsjHKvQx9qGnCAYC/O/tl1g2fRoinEBJSIQhwASJiSnjWEYUDHOFiIRqqnj+17MyIuJAERpCAalJTCdIj05Oj26MOeEc/LYz08ZmrTx9z80EZ85Hi4BpGpgyBBikACHcaCILHB5SuW0zlNtRTBuZRx+4jfluyeLCPBZ4S1iilq2IEulmLOLIqb8xduz1bWrD9D9+57N778eMRZEygirysPJ0xo57ar3qCQYCvPfSEzTMmweRFGoChAHSAkuaWCSQMgpYK92p/G0mkhYRMJ2AVye/Vy8OPvb0zVpEPnzzRWZ9/TV6yIIUWDKFInSkS6J0y+f861ebdcamk5KeHYxrMTuIIWU4c1VBFX5URcd0g1aez/EXXtUqfyN2mGs7MX/ObF778EWWFflYmFvAAmcZDUoBAA6ZoLuxiJ7NtZTVNrJ1VtsnG5szazrjb78TM5b+h6eKXKxcnVNvvbvNBuO66ko+eO0ZmhYsQkSXi4jIiIiBRXI1IqKhCA+qcCJUgekGmefhgFPOoU+/AW1ia3vx+rPjWPrzr2hhC1IiPTuToRXXhXCj4sEkgpTxzHdZaIoHyyVx9SnlrCtvaS/zbTaQJ++8geY5C9Ei4m+zAwAhPGjCC7og5VPY9pDD2GmPfdrEDlsgNhIfvPoCPzcuYGlRLguyi1ioVxAXbgByZSM940vo1lRP19omDttzzEZLD7Fo4XzeufFmZDSGJZtRRS4yR+eU29pOGNaHuupK3n3+MZqXVaFEUyiJzFKWZWHKBFI2tyitoIhsVOEEDUy3RCnwMfqMCzuFA/35h+6g/s9ZaBEJhlzpKTE9MKjCi6IKDC94epRx5hXp5cZgIMDTt1yFWheHJKSsEOn1PdK/E8WN5ZH4tuzNKRf+X3t0z2Y1TP/jdz5+ahx6UwqSYoXvIE2L2YFHopUXcfwFV2y0v01bINqA9MlVD7LEp7OoIJ/5nlIqlVKkUBDSpNxaRo9IFRX1jfSImBxz/MbfRLRo4XzeufkmZDiBJUMoIgd8Dk75b8cQhnXly/99wO//+wA1ZKAkwLIsTBn/28AKakY4HBnhAK1LLkedc0m7Oc2fvPMGmucuQouCNGRmUIisuC5EFqpwIzSB4RXk9O+5XgP7ooXzefv+/6I2JJEpiWEFgRQAivCjKU6MLEnpdttw5Knnrbkym1Xy1afvMnPyz8Sawoi4iZpS0FI6quFEsZwIVCyRwFKTmKqBpRqYmkVETeAJG6hRgWUamDIImEDL2QGkfCojDz+KEbu0XyCxLRCtwLRff+Ldbz9iaXEOC3MKmO+sIChygHSqgp6pRfQI1dG1NsCILv3Zd8wRbWbL2qirruSla65GNsczwuCHbAfH3XTLJhVh9PHbrzD96y/Rmg1EUiBNC5NoZslqORqqyEYROuhgekAvKWzVJ7RgIMBLD99BYnENapS0HTKClLEVZdLi5QJdYGRB122GtvqgPWfWdN5/+F70gIlMmRgyyPIlC0XkoKoOzCzouetOHHz0ya3adkcnGAjw7dcfsmjaNBKhZkQCtJSKmtJRTSeK5UJID0J6kMKLpWZhql4sddU5sABUI4qQJkGtBkX/GUfIgtTKDwJ/nx0kPVm4Ggbi1RSQMSRxpJJAKgksJS0ypmZgahKpSYRLw5Pjo+eAIWw7co82ebCzBWIDePWZcfyRaGBpUR4LsopZqFWsSJhWYNXSM76Ubo0NlNU1c/SYkzpEfqRgIMDT/xkLzUksGUQRPkS2i2NvunmTEoa18fYLT7Dg55/RwgZKQmBZZkY4Yi1K6agiC0XRQQPDI/B068LRZ126xj/CYCDAc/fejFUVQI2CZVoYMgzEMyUEivClZzK6IOUT9N5hx3YZkH/58WsmvPgCjiaJZZgYMkDa1yNQRQ6qqpPywVYHH8Lu+xy40e3bUIKBAJ999BpVc+eSCsdQUgItqaIa6cFemG4U6QE8SMWLpXgxNC9S0VddobRQjSiqGUHI9EuKKJYSw1LjmFoSQzMIK3EcSLSYRE0ABljWP2cHqvAidEEqW2AYGj7Th2Lp6RmHdCKkC4QbiQupuDEVF6bmhlWkNPm7nSaqEUO14ggrhpAxII4UcRLuas599L4N+n3aArEWmgIBnnzmPpbleViUn898T1eqlfSpZqo0qDCX0CNSTUVdI70NJ6edfWlrmv6vCQYCPH35JRBKYskmFJGN8Lo55JqrO8W6/Mbilcfup3LqVLSwmZ5xSDPzpB9vUcqBKrJQFQ2pgeEFJSVRYgJpmhiyGUhmyippMVAcoIPhV9hqv4M77GD77Rf/Y9Jbr6OFwDIMTNlEWjAUVJGDommk/IJdjj+eYdvvulFsWrxoHt98+j4NSxZjRlKoSVBTKqrhyDzZuxHSA6QHektND/arHUyliWZEUMwIihUB/hrsTTWBqSUxdRPTIVG9DvK6lrHLPqOp6NaLYCDAW8+PIzB3IWrEQE0KpLk8mCK20gMGpP+teNOzAy84uhVzztWrPO5mrQQDAX785n8snD2daFMIGTcQhkA1BKqhoZoaiuVAWM70i79ExlLcCGsmpz93zQa1bQvEGlh+rvPyE6y8spmeycX0CNbRtaaJvQbtwIjdR7W2ua1CMBDg6SsvQTSlMGUgvabt8bDf5ZducpE+bclzD/6Xhhmz0SISkQLTMjBlhOUO4DQqqvChKDrSCaZfY+ejjtloA2lb8PHbrzDz089Qm8EyUxnBkICKJnIQukoqR2Xf089lwOCt1lrfnDnT+P5/HxCorkLGDdSEQDM0VMOBYrpWDPZSeDPLOF4MzQOZbLArIywDzQijWBGEFQGiWCKKVJcP9ikMh4mlS/QsNyW9e7PLqIMoLF79bHnOrOl8/NJTGHUB1JhEpARYElOmsGSE5T6cFTYIDwouFEVLbwp1gOlV6TKgPwcceXKn8uWtDlsg1sBLTz7EW8UuujU00rUxwmknX9ThM1IGAwGeuuoSlEALYXC7GXXxhev0h2yzdoKBAG88+QChBYvBpbPPSWdt8r/b158dx7KJv6CFwbASWDKYuaKjKX6ErpDKFpiqmg7GsBwIIxs1VoCgKw5XwWrrVswkqpl+shcyDESxlCiWEk8P9noKQzeRTnD6suk2cCA77XrABg3AH7/9CjO+/wYRiqMmBBiZiDgSGd9Ay5BqBUVkoQgHilDS+3HcIPxuttn7wDYLLe1I2AKxCXHPOaeiNKYwZSNCeFHdXnY590y2GrbpHiBks/7U1VTy1f/eoWbRQlKROCJhoaUUFENDNXWUFY5ZN+BGCjdS8WCq3vR6OBByfoE7UokSFZjW3/dm/BNB+lwwDSEUBCqgIFAy7wIhRKaoWPEmReZWIUEIpCKRKkgFUASWKkBVELqG6nSgezxk5eSSjMUILlqCGrVQksAal4KcqMKT2dkv0rMAt8BVWsThp52/WfnnVoWd7nsT4O7zTkVrMJBWA5bwoLuKGHn2CZ16icNm3fj2q4/49bNPkFED1VBRDRVluUPWcqFIF+lB3pMZ5D2YqhtEfxT642xZmQBTA0tJoRpRFCuKIqMIGQKrGlXGEVY6bDMraWB6vBh+iXBlY6RycDTFEZZEmCAsAVIipEDK9BiPTC9SkXnwlFIisZBYWNICLKQ0kdIk7dg11ul3IDMlDaDl8L+8FiG8qLjQlGxQs9P5wbw6ZYMHbzIhvsFAgI+efIzUsiDepIcsmYVHycKrZdGYqmPUuAtavU1bIDo4d59/OlpdEmQDpnCjO4sYfuox7Ro3bdM2vPDY3dTNWoAzquGI+1GsAiy1mKQjD12M/ltZSwOpmKhmFMwoQkbTSzdWXWaQj6dj87UUpmZiahLh1vDk+ug7ZBuGDt+lQ6yfBwMBFsybzaI502moWkYk1IQRi0MyhZUyEZaVESPAlOmJRmaFyHQJyPGw/X6jN4m/h2AgwPinniC5NIA36SZLZuNRsvCoXjx6Fh41i52VHcBF+gXEzShRI4ykbVaC7CWmDso9F56BWpvEsOoRwoXm8DHomM4VjmjzT+pqKnntsfswGiI4o270ZC5CFmLoxRh69opyiplET9YCtZhqA0lXBMNpgFPF6cumon//DV6jt2kfgoEAHz/7JIlFDXiTHrwyC6+ShUdLD/4eLQttpVDchBkjYoSJGWEiZpioDNOshIm5k3TbYSh7HPLv91vZS0ydiHsvPhOlOoG06jFwojuK6HfEPux9YPttvLNZf3747kt+/OhdtJDEEctCNfJAFJNyFCHUfdABSwVTa0ZL1aAaf2IpARKeGFq+l8POvGiN0Tg2HY9gIMAnzz9DdGFdegZgZWVmAFl4NC9eLZsdle3/NgNImDGiRpjmVJCaxDIiVjNhNUzMlaJ8+CD2PPLYdu2TLRAdhHsuPhOtOoll1WFlhKHnwbtzwGHHtbdpNmvgxSfupXbmXJwRHT3hQzULsdQiko58HJllIUO3UGQjilmDlppHUg2S8BgU9uvB8Wde0r4dsPkH9dWVTPnhO6pnzyXZFIaERDMEuqWhWTo6Grp0oKHhVNzpJSAtPQMYqQwHJyx3/CTMOFGjmbARoi5eSWT5DMCVpMvQfux77Ent2dW1YgtEO3PPBaeh1hlIq54UDhx6EV33HsGhbZzZ1Wbdqaup5PUnHiBZF8YVdaEncxAUYWjFGPoQnAwBwNBTKFYNwlqClvqdhDuMmQ1b7b4Hu+1lzwDbiqUL5jPl269pXLw0vbM6AZqhoFkautTQpY6OA01o6Oioio4mdDShoSkrvzvQhE4/UUI/0ptlcWReqyBpxokYYSJGM3XxKiIyTFgJE3UlKBrSh/1POGVj/RraBFsg2oFgIMBT/3cpaqOBlA0rlpK67DGMI046p73NswE+++hNZn/wLa7YQJKOClD3wkF6WcjQwmhGZllIBEh44qgFbg4+8Swquu3d3qZvcgQDAd576AGc1RZ5soAcLZ8sPRs9M5hris4Q+gB90je0eIJfmZSVxLBSGDKVeTeImzEMoxlDpjCtFCkMDJkiRRIDg5RikFJSmKqJ5RRo2W78pV3Ycocd6bmJb0i1BWIj8tfOZwMpGzNRSYX0Gb0X+x56dHubZwPcc/n5ZFcXYepDQTsYS1ShpX4gqYZIeJPk967gxLMvb28zN1mCgQDvPXg/rhoyYpBHjjOf3dXdIRssadGcCtCUbEwP9tLAlClSpDBE+j2lpDCESUq3wClw+L0U9+jOVrvsRtlmvudhfbEFYiNQV13Ji9f8HzSnsGQTQnjRXUVsfcIRm8VOzY7O+68/w5Iv/sAZH4zTfQiGI4me/J2ofwnH3XYz/tz2dRRuqgQDAd659168DSp5Mp8cPR+/I4/d1T1WiEEo1Uh1bClNZgMNjgC99t6BHfcb3d6mbzbYAtGGzJk1nY/uuAsZSWSyq2ajeQrZ5dyz7J3P7UwwEOCpW67GV1dKyjEU1G7AUoT1IYW79OWw465obxM3KYKBAG/fcw9ZjTr5Mh+/nkeOI5891VEZMTAJpQJ/iYEzQL8DdmH7PQ9pb9M3a2yBaAN+/+UnJjz8KFYsjiWbUYQfNauI/f9jJ9Frb15/7mHqv5+HI7k1TtcYUo4EenIyoaIqjrvtJvy5J7S3iZ2eYCDA23feRXaTkzwKyNHz8DvyGaXuBdlgSpNQspHK2GICZj2NriADD96T4bvaYtDRsAWiFflhwudMevpljEQEKSMoIgfFV7jJHdTT2QgGAjx189X46stJObZCqv1BLgI+oHy/oRxwiH0854ZSX13J+w88jK/JRR755DjyydHzGKXuA760GASTjVTGFhEwGwi4mtj68H0ZPPLQ9jbdZh2wBaIV+PjtV5jz7qcYyTBSxlBFHjLX22HOfN5ceeGxuwn/UoWWGorTNQZDj6GnJhHKr+PcRx9ob/M6HcFAgLfvuBNf0EOe+MtnsJeyXAwMgslGlkYXEpD1NLpCDD96NIOH22LQWbEF4l/w+rPjqP7iF1LJIJBAE/mY+T5OvuUuWxjaibqaSl687WayG3uQdA1GqlsjkvORvEevQ3Zi1P4bdqjK5kp9dSXjb3+QsmQ5pe4KRun7psXAMgimGlkamU9ANhBwBRl29CEMHr5be5ts04rYArEBPP/QHTT9OJNUqglIoikFGAV5XPjgE+1t2mbLM/ffTGJqCNUcisN5GIYjgp78gXBpgHNvvb+9zetUzP7jdyY98SblVjdK3GXs7toP02lSF69iTnQadZ4AO554FIMH22KwqdOmAiGE2Ae4n3Si+CellLevdL0CeA7IyZS5Qko5XgihA08CQzM2Pi+l3LCz/FqRJ+64jtiUJaSMRsBAUwqxuji48N7H2tu0zZK6mkpevPUWfIFeJFzbITUVPTkHS0xk8LF7s9Nu17e3iZ2G78Z/yLKPJlEmulPiLmMX794YVoqa2FKWRn9BH1bMQaec0d5m2mxk2kwghBAq8DAwClgKTBJCvC+lnN6i2NXA61LKR4QQA4DxQHfgcMAppdxSCOEBpgshXpFSLmwre9fEQzdcjjWrjpTZAFjoSiFmmYsL7xzXHuZs9jx25zUwM4WwtsHhHIOhN6OlviVaFuLcm+5rb/M6De8//TipX2ooU7tT5u5K9+w9SZpxqmJLWSoX0XX/Yey43wnYAdmbL205g9gWmCulnA8ghHgVOBhoKRAS8GV+9gOVLb73CiE0wE36lPg1HWfVJtx/1YWIhc2kzHoAdLUAuvu44Nb7NrYpmz1z5kzjowcfIbupD4Z7F9AVXNGZxNVvGHHqGIYOv7G9TewUvHzn7WTNF5Q6Khji6ovq60/MCLM4MpclYjHDzziMHQZ3zDPYbTY+bSkQXYElLT4vhX88jFwPfCqEOB/wAstP/XiTtJhUAR7gYill48oNCCHOAM4AqKioaDXD7730bNRlcQyrHlBwqAWoA4o55+p2X+Xa7Hj41ivQ5ioItkF3HIqhhdCTX5PoHuPU6+5ub/M6Bc9eey1FdX5KnRXs5ByJ8AuaU0HmNU9nib6E/a88nz5d9m1vM206IO3tpD4aeFZKebcQYgTwghBiEOnZhwmUArnAt0KIz5fPRpYjpXwceBzSBwb9W2PuuegMtJpUJuW2hq4V4tmqG6dddt2/rdpmHZj+52988daLGIE4jpgDV6wXuPfE0MEVm0nM8xW7nXcqAwaNbm9TOzTBQIC3bvkvZdESSpwV7OkcBX5oStYzM/Q7Sz1VHHr1f+ife0B7m2rTwWlLgVgGlLf4XJb5riWnAvsASCl/EEK4gALgGOB/UsoUUCuE+B4YBsynDbj7vNMy5z2nU27rehH5Ow7i2LPGtkVzmy3BQIB3X32chnmL0WMKetyDZvgQ5GEp+SQdeShi/3RmZQGmFkBLfYnR2+LUq25fW/WbNfXVlXx0+4OUJ8socVewl74/+KA+Xs3U4CSq8xs5+fabGdTehtp0KtpSICYBfYQQPUgLw1GkB/6WLAb2AJ4VQvQnfc5SXeb73UnPKLzA9sB9bWHkPWefAo21GLjQHUWU7WWfxfBv+Parj/jli88QzQaOmAM9mY1i5SKVfFJ6PpY6HCfDATB1EDKEatYjrIVoqd9JOSIkPQauAj+HnHoehcVj2rlHHZcFs6bz/UMvUW5VUOKuYA/XfliZcNS50emEuhkce8X/sVV7G2rTaWnTM6mFEPuRHthV4Gkp5S1CiBuBX6SU72cil54Askg7pv8jpfxUCJEFPAMMAATwjJTyzjW1taFnUi9aOJ93b7iJLQ7d1z7Wcx2oq6nkzefGEasJ4Ihq6AkvqukD8rHUfFKOnL+VV8wEeqoBYTVgKQEMvZmEKwE+lYEjd2LU/oe1Sz86K5O+/oJ5b3xDuehGF3cZTtWNYaWojVey1FiEsnUeo08/u73NtOlErOlM6jYViI3JhgqEzT95//VnmDv5N9SwhSPuQjN8KFYulpJPypGPbHmwurTQk02oVgOSRkwtSNIZJeU2ya0o4dATzl2vXeXBQIB3bvovXWOluBQPpjQwMf96x0j/J0zM5S/FwNAADYRLx+nLIqekiF5bbkXfwVu1+u+nNaivrmTun39Qu3gxkcYmkuEIViIFSYligTAFqqWgoKBKBYfloItWRrG7K7riIGnGqc6Eo3bZZ0t2PtCeadlsGLZA2KyVR+64GmYn0FNbknCX/e2aakTRUvUI2YipBkg5wiRdSUSOi50POJChw3f9V23P/uN3fn7iDcqtCrq4y/BqPqSUxK0oqtAyL3WD6rZkRlyWv1vLf275vYklzczhMyYWRvpnDEwsBKQHajRUVBShoAgVBRVVqChkPgsl/bnFd+ryskL961rm5/UlZkaoii5hqVjM4BMPYNBwe4eCzb9nTQLR3lFMNu3Imy8+Ss13M3FFt8By7wqaAqmFqKlPSLojGF5B2YAtOOLEc1u97S/eeZ3Gz2fQVaRFYVfvPiuWSmZG/iDWV+Ooiy9bUX7BrOnM+/036isrSQTDWLEkpEAxQLNUVKmiyfQQrkk9PZyL9ICuomUG5bTQLH9pQsepujIDt5Yun3kJIVZru9VCVJa/p3+2sDCxpJU5vjKOJa30i3RZKdPzIEta6ffMvMgkPRuyhJWZGVlYQmIpFqYqEQ6FUeecxs497AOmbDYetkBsZvzw3ZdMevVtvKGeJFwDQemLpdagpb4gVp7k3Bvabm/By3fejneBpFTrRm9XCWp2CQkzRlVsKctWLJUct8p7e/QbQI+NdJZGMBBg0YypzJ82jUhDI0JV8eT46dKjB2W9+lLRo+dGscPGpr2xl5g2A+pqKnnx9lvIbizF0Idgah60ZAjF+p3mwjpOvermNss++8wVV1ESLKTEWU6eswghBOFUiOrYEpZoi9nx3OM22sBvY2PzT+wlps2QYCDAU/+9jqyaHBBDcDjGkHLE0ZN/EPMuYa9LzqFPn9Gt3m59dSUf3fYg5akyurjL0+mh/RBI1DGj+TeWuas59Or/sIWdDt3GpsNjC8QmxsM3X4G6QKKZg3G6RmPoJs74dBKuCQw4dHd226v1z1qe9PUXzH1zAmWk/Ql7uPfDcpnUxauZH51JY0mUE26/ni1bvWUbG5u2xBaITYAXn7iX4C+LccX6g2cvTB00Yx7ID8nepivHn3lJq7f57hOPYE1uoFSroNjdlZKsUaSsJDWxZSwzF6NunW/H49vYdHJsgeikfPXpu0x97wu8zb0zzuYhSKUKNfUJRjfJadfe0eptPn/j9eRVeSh1VDDUNQDFrxAzIiyJzGeZWMzAY0ax/cgTWr1dGxub9sEWiE7EnDnT+PChcWQ3lZNyDEZVD8HUmtBS3xEubuK4y2/An3tsq7UXDAR4++Y7KIuW0MVVzu7OPcAPoWQjc5r/ZKljKftfcT59utihlzY2myLrLBBCiK5At5b3SCm/aQujbP4iGAjw1G1Xk11bgKVshcMxBkOPoSd/pzl3KQdeeQkV3VrvUPilC+bz1T1PUW6WU+IpZ5S+H9InaUjUpJO+5dRz8j23Yscd2dhs+qyTQAgh/gscSfqwHzPztQRsgWgjHrzhEhyLHCjWYJyuMaQcqbSz2TOfoccdwogdr2r1Nl+99076LerGbt59MC1jxaa1aC+Voy/9j530zcZmM2NdZxCjgX5SykQb2rLZ8+yDtxOdWosjMRDFvT+GbuGKzQV+omCHPhxx4sVt1vbTl1zBDmInnA43k0Pf4x3Vkz0Oab3lKhsbm87HugrEfEAHbIFoIx4+60KEdQBSUYGlKMbH0Fvn1CtubfO2Xz/3enb37kXSSjAh9RknjrNPzrOxsVl3gYgCvwshvqCFSEgpL2gTqzYz7r3wXBxyNM74HEJlszj31vs3SrvBQICfrnqekf49aEjU8GeX2Zx4uS0ONjY2adZVIN7PvGxamXuvOB93ZD+0VBXFh2Rz6iEbRxy++eAtHJ9GGOAfyqLwXByHVXDsrq3v17Cxsem8rJNASCmfE0I4gL6Zr2ZljgO1+Rc8duc1eGt3BeKYg+ZwwCE3bZR2n7/xerYODCbH052pwUmMvPXkNsvFZGNj03lZ1yimXYHngIWkT3grF0KcaIe5bjgfvvMC6p99MHQXscJPuPiyBzZKu89fdCUj9V1RdJXvIp9z9CM3bpR2bWxsOh/rusR0N7CXlHIWgBCiL/AKsE1bGbYpM/3P36h5J0jC3Y+k610uvu3hjdLuu+fcwi7ZexE1Ivyofc3JD9n+Bhsbm9WzrgKhLxcHACnlbCGEvqYbbFZNMBBg4p3jSXhGAO9z8f1tLw4LZk1n0f0TGObbmdrYMhYNbODkM2xxsLGxWTPrKhC/CCGeBF7MfD4WsA9f2ABeuew2TPc+6MkvOOPp+9q8vfeffpzSKdn09g1iXvN0Ss7cjkM66DnNNjY2HYt1FYizgXOB5WGt3wLj2sSiTZgHzj0fVTsEV/RXDrqz9Y/xXJlnrryK7ZLbk+Xy8WvoOw4ed2Wbt2ljY/PvqKup5qtvv2busqU0Jk3CQiOqOIioDqJCJy514lIjYWkkTY2UqdLFHWLChas+jfHfsK5RTAngnszLZgO4Z+x5uFIH4kzMZevzh1NYXNqm7b183jXs4t4NS7GYEPuM48fd0qbt2djYpKmrqeajz8Yzb8lSwkIlqjqJqc41DvCmoWAaAmmANECQDfRffSMqCB0UTaKpFipWm/RljQIhhHhdSnmEEGIq6dxLf0NKObhNrNrEuP/asXiCe6GaDXh2iTF0+K5t1lYwEOC7q55kR9+ehFIBfsmazEl32uJgY9Oa3H7rlXzl7cuyZM5qBvhioHj1Faw0wDudKRweE6di4BQGbpHCbSXxyBReM47HiONKRnHHmtCjy3A6VRAudEcuOQUlbLXN7m3Sz7XNIC7MvB/QJq1vBjz3yH/xLNkOS5HEe/7G6afc2WZt/THxG0Ivz2GIf3sqo4to3s3BSYfYYaw2Nq1BMBDgnruuY0LRtiyM7gQhieIFXTPXOsC7jQTuZBRnMoxbRinqWkr/ITsyYMCwDr0HaY0CIaWsyvxYD8SklFYmxHUL4OO2Nq6z8+1XH5H6MR/D4Sfm/5Cx1z7UZm29ctcd9F/ag+5ZfZkZ+p2trh1DQZe2XcaysdkceP+tZ/l5yh98kTecSrk3ogqyChLsZszkwSs2bb/eujqpvwF2EkLkAp8Ck0in/7bTfa6GuppKZj8zi4R7MKb+LmPvajtxeOaSKxkpdsLpcPFj6CsOG3dtm7VlY7O5cM+1J1KrlvG5bwh1qT2gBvIKY4xKzuKOS/+vvc3bKKyrQAgpZVQIcSowTkp5hxDi9za0q9PzzhWPkvLsimKO59yH2k4c3jj3BnbzjiJpxfnK+IyT7UysNjYbzB+TJ/L1M9exsMvOfOE/iKY6FySgS1GYA+Rirhl7WXubuFFZZ4EQQowgPWM4NfOd2jYmdX7GnTEW6TwAR/xbjri3bZ406qsr+e3GNxnh252GeA3Tus7l5MtscbCx2RDG3TkW9x/fMmnQ4Xxdfi7RRh2iUFES5AhHgPPPbvuw9I7IugrERcCVwDtSymlCiJ7AV21mVSfmvgvPQxejcUWnMuqm49rEAfXNB2/h/DRCf9/WKzKxHrPrFa3ejo3NpkxtzTJevONMimbM5Zfdz+T7AdeQCKigC3qV1HNqscoxR23eq+jrug9iAjChxef5/LVpzibDPZefjzu6P3pyCRVHdqWiW69Wb+P5G69n68AQ/J5u/BH8mR1uPaVDR0HY2HQ0Xnvxbpq+foviWc1MOuRMJuV1x6gXCBcMKKnhkoFl7LHnCe1tZodgbfsg7pNSXiSE+IBV74M4qM0s62SMu/UqvHW7I2QYtl7GqP2vbvU2nrvgSnZwpjOxfh/5nKMf2TjpwW1sNgXu/r+j6TpjGq4mP/876ESmVJRh1YLigaFdlnLdzsMYMnT/9jazQ7G2GcQLmfe7NqRyIcQ+wP2k/RVPSilvX+l6Bek04jmZMldIKcdnrg0GHgN8gAUMl1LGN8SOtuadV57EMXsAhqYTK/6aiy5q/UN/3j37Vnb17U3UaOZHbaKdidXGZh349usPmPL63fSdVkOXvC14breLmNlQjKwGLVuyffEibh2zP90rbGFYFWvbB/Fr5sdfyOyDABBCqIBzTfdmyjwMjAKWApOEEO9LKae3KHY18LqU8hEhxABgPNBdCKGRTgx4vJRyihAiH+iQBxRNmfwTjR8nSbq6kfC8w8W3tG6KqnQm1m8Y5t+JmtgylgwKcPLptjjY2KyJ+246nbzpvzJgRgx16LbcfugpzK8vgEqJM8dkR9987j7lRHJy7T3Aa2JdndRfAHsC4cxnN+n9ECPXcM+2wNyMvwIhxKvAwUBLgZCkZwgAfqAy8/NewB9SyikAUsqGdbRzoxIMBJh0/5ckPNshxXtcfF/risO7TzxC+Z859PYNZG7zdLpdsBPb9BvQqm3Y2GwqLFo0l3fuPY9eM5aw9yKL9/fZk8e3H8HSWj9UgScvyW5iLg9fdml7m9ppWFeBcEkpl4sDUsqwEMKzlnu6AktafF4KbLdSmeuBT4UQ5wNe0iIE6aNNpRDiE6AQeFVKecfKDQghzgDOAKioqFjHrrQer176Xwz3XmjJzzjz6dZdVnrmiqvYPrU9HjsTq43NGnly3LXw8yf0nx5i7xC8OuZAbtp3G2pqs6AKcgrjjLLmctdll7e3qZ2OdRWIiBBiqJRyMoAQYhsg1grtHw08K6W8O7PP4gUhxKCMXTsCw4Eo8IUQ4lcp5Rctb5ZSPg48DjBs2LB/ONHbkgfPvgBFH40z9jOH3/efVq27ZSbWb2KfcLy9+c3G5m8EAwGevO1Uus2cw3ZzDTQLnjn2cD7zDqaxzg01UFgUYT9rITde0rp/n5sT67MP4g0hRCXpM6m7kE61sSaWAeUtPpdlvmvJqcA+AFLKH4QQLqCA9GzjGyllPYAQYjwwlPRSV7tz78Xn4DRH40zMYtiFu7VamGkwEOD7q57OZGJt5Fff75x4py0ONjZffvY2v339Bs5ANb5AkLIlMfarh6gTHj/hBL5StyDc4EDGoLQ4xKFqDZedf1F7m91mvPbQLSSDC3H6Iuj+IEbQz+Hnv9zq7azrPohJQogtgH6Zr2ZJKdfmNJ4E9BFC9CAtDEcBx6xUZjGwB/CsEKI/4ALqgE+A/2SWsZLALsC962JrW3Pv1Rfhbt4PzajDP0owZOjKq2Ybxh8Tv6H55bkM9m/LsuhCwrs5OfGQG1qlbhubzsKMaZP5+I0H0BqWkd3URE5jjPwGk+Im2C9TJqnC3EKLV087nW/MPsQDGmiC7iWNHOOPccZJp7dnF1qVz157mso5P+PKiqD7Q2jZjTiyGikY8FdAZyrpJJrqt4ZaNpx1EojMQD0W6CalPF0I0UcI0U9K+eHq7pFSGkKI80gP9irwdGYX9o3AL1LK94FLgCeEEBeTdlifJKWUQEAIcQ9pkZHAeCnlR/+mo63Bk/fdjHfZDkhhkOg9lcOOu33tN60Dz157LUObh9Atqw8zQr+x9bWH2ZlYbTZpgoEAzz16DWbtfLKaGshpjJLXYFAYgH3NdBlLQF0O1BeozO3rocZXSFX5Dsxxd2FuuJBUvQIOQb/SWs7t5uOgg1v/RLWNxZQfJvDbF6/hckVw+JtRfQH07EachWHKCtNlTEMjEckjVtudUNBPIuTFldODw8+9qs3sEunxeC2FhHgN+BU4QUo5KCMYE6WUW7WZZevJsGHD5C+/tN0x2Z999CaLXm8m5ehCLPdDxt7xcKvU++p51zLcvRMCmBT9jiMftmcNNpsWT4+7jsD83/A2NZDTGCY3kKKwQeJO/lWmMRvq8xWa8lyEcv0kc4rJKh/BpIjJXK2Q6ng2sZCGyIiH4oEt/ZVcPmwLRu6wU/t0bANYsnAuX748Dl1twuEPo/kC6NkNOD0hhEiPxZalkIjmkGrOwwzmkAhmYYh89j/pojZ5cMz4d4et6tq6+iB6SSmPFEIcDZDJ7CpazcIOzuJF81j82lIS7kGkHO+2ijgsmDWd+fd/yY6+UQQS9fzi/ZWT77y5Fay1sWkf3n/rWWZP+gh3Uw3+QDO5jQkKGiQjon+VaXZDXb5gxhZOgrnZRHOK6Ln1Hhx61Dnc/dB9/JLSWSDyqY9kkVooEKSXEPRsSUlhMz1kA8P0FJecd1E79XLdaAoE+PDpuyFRhcsfRfU3oWc34vAEKNs+rXJSQiLmwwjnEavqRaopi0Qqi5EHnUzfLYe2cw/SrKtAJIUQbjLpNoQQvYBEm1nVwfjkuhdIenZGWB9y0YP/PnX3y3feTv9lPennG8KC8Cz00WWcvKctDjadg19//oqv338CR1PaYZzTGCe/waJPCPpkysT1tBAs6K4TzMsikpNPdrctOeK4y9g2N5e6mmrufP45ZjnyeLEyl0uu/wgZT98tFXD7DcpKmult1HFQ724cdPCh7dfhtfDe0/cTqpyGMzuK7g+i+RpxeBsp2fqvKVIy4SEZziNcP5BUMJt4xEvv7fZmzwNGt5/h68C6LjGNIr3reQDpDXI7kPYXfN2m1q0HbbXE9Mjpl2Cp++OIT+D0Z//98s8LF1zF9o6d0RUHv4V/4JBxm8fBIzadi0WL5vLeS/diNCzCFQ6SHYyQHUrgb7IoDICaGTYMBeryoDFPoynXQzg3F6WwB0eddjVFxV1X1Dfx+295YeJPzHEUUpnwEw3pYGQuOgTZvjhlehP9kvWcsf++DBiw5cbv9BpoCgT47OVHiAcW4syKoflDqFkBHFkBHM6/pkhGykEikocRysMI+kk0e/CVDuTgUy5cQ+3ty79aYhJCKEAucCiwPekw1wuXh6Buytx//nloymhc0SmMuunfZXesr67klxteYxff3kSMIN+ZE+zDfWzalT8mT+TLj57GaqzCHW4iOxQlO5gkJ2iRF4RR1l9lkxo0+KEpR2FhTzfNOTkYeSXsOfpsdh/6z4QKjz/7BN81xpinFFAXyyYRUhCyPwCKFwryI3RXAgwyg1x08unkdJCMxPXVlXz0wgOoqTqcvhiaLyME3gB5/f9aNDENjUQ0l3hDGZFgDvGQB+EsZf+Tx3aYvrQG6zqD+GV1CtNRaO0ZxD2XnYe76QD05DJ6HZvPbnuN3uC63n3iEbpO9VHiqWBpZD7hHTX2PHLzzjNvs3H44fvPmPjpi6jBGjzNIbJWiIAkLwRKiz//mAMa/RD0q4T9TsLZWcSzc3AX9eKwEy7524ygJU2BAPc98wR/qn4WWrk0hr1YkfQ1KcDpsyjyNNPTrGeXAg+nnnDaRuj5mpk9dTIT33sWhx7GkR1B9QXRsgI4vU2oqrGiXCrpJBnJwwjnYIR8JJs9SEcRB5562SYjBK3hpP5cCHEp8BoQWf6llLKxFezrcDx043/wNu6JYoXQhtWx216nrv2m1fDcxVeynbIjbreX34M/sNOtp9vnN9i0Kp+Nf40/fvgALVSbFoFgDF8oRU6TJKf5r/0DABEnNOZATbHG3D5OIr5sElm5+LsP4vCjL2boOvzbnD59Ko9/9DEznYUsS/ppDrkgOTB9URN4/UlKfUH6JOs4buR27Rpl9OOXHzFj4ke4XVF0Xxg1O4ie1YjDHaJ85F9TpGTcSzKSS3jJFhghH4mwG29RHw49Y/M6YnRl1nUGsYBVnwfRsy2M2hBaawbxytP3E/6mEFPLJl7yBRfddN8G1RMMBPjmqscZ7NuWuBnjR+NbTrzPXlKyWX+CgQCfjn+exdN/RAvV4W1uJjsYwxc0yA1K/JG/lw95IOAXhPwqzT43UV82yew8SvpuzzEnX7JObTYFArz29uvMaAxSo3mpV7w0WR5CCRfxkIrIjK3CDTnZMSrUAP2TjVxywokUFndp5d/A2vnwhUdoWDQFlyeK7m9GzW5C9wZwuJpZHm8ppchEDeViNOeQCmWRiHqoGLwzux189Ea3uaPQGjOIAcA5pPMjSeBb4NHWMa/jMHnS10QnuEg584l732fsTRsWzvrFO6/j/iLK1v6RVEeXsHiLek48xxYHm38SDAT44J3HqJn/B0qkCWe0GXckhieSxBsxyApLfGEYlIRBLe5rykqLwMJuOmGfm4jPRyq7gD5b787Bh63bTuI3336DH+fNp1b1UKd6CUgPzSkn8biOGRdglpHOkJNBF+huk5KiZnrKeoY7TC4+d+M5X+urK/n4hQcQqQYc3iiavxktK4DuDeDuGqMsswJmWQqJSA7JYDHRJX1Jhrwkk1623uNwhuyxy0azd1NgXQXiOSAEPJD5fEzmuyPawqj2IBgIMPmhn0m4h2Ep7zL23g0Th2cuv5LtUtuT5S1mWvBXtrnuCIbZu6I3S2prlvH+W48RXDYHNRLAGQ3jicRwh5N4IybZYYk/DNsYf7/PEhDMguYsQUOeypIKnajHRSzbh+ErZMiIAxm139pSoaUjhz76cSKVwkm9mkWj9BAyXMQSDoy4AikPf5MdFVS3xOVKkZ0dJ1eJUWiEKTZj7NS3z0YLNX3nyXsJVc/C6YmhZ4VRs0No3iYc7iCl25gryhmGTjKSS7y+gnDITzLkwSSH3Y4+m/LuvTeKrZs66yoQg6SULQ8i+EoIMX21pTshr15yJ4Z7T9TUJ5z71ANrv2EVvHXOzeyatSemavBt5DOOfcTe27CpsmjRXP739uOEaxegRYM4IxE80RiecApv+K/Bfwfr7/cZyl+Df22RxsIeOlGvm7jHi+nx4ynszh4Hn8DI3msP85w7ZzavfPgeS0yVOj2LRjwEDTfRpINkXIWEpKUASAVUFzhdBrmeCLlKjAIrTJERZcviPMYcOGajOV4nfPgGC37/CocjhiM7ukIEdE8QX88EvszitWUJkjEfRjSHcF0ZRnMW8YgLzV3KfidftMk4ijsq6yoQk4UQ20spfwQQQmxH+pS5TYKHz7oQHAfjjP3A4fddsd73/zHxG5pemsl2vl2oj1fzZ/EsjrvSFofOzuy5U/lw3JVkBRszyz4pvGFrxeC/80peuaQKwWwIZwmqSjTmeR3EvG7iXi+GJwd/SS8OPuJctlxNNNDKzJ0zmzc//oBFCUm97qVBeAmabsJJJ8m4ipUA0eK5TQKKCxwukzx/hBwtRr4VoSgVobdH57jDjtyo/oHZUyfzwwcvookgjuwYWlYzalZaBByeCOUtomOTcS+pqJ9YVS+aw9kkm90Y0s+IA4/rMLuKN0fW1Uk9g3Qm18WZryqAWaS3ukgp5eA2s3Ad2VAn9T1jz8MVPhhnfDYj/jOSAYO2Xq/7lyfay3MWMTs0lbKzRtJ38FbrbYdNx+He608hb/YU+s2KrnAAJ/T0un84SxDJ0ohmBv+EJxvL46ew+0AOPuzc9YpQ+/Cj95k4YyZVioNG1UsjHpoNF9GkTiqxXABWuskhcLhN3I4kfi1OPhEKU2HKVZMxe++z0TeYNQUCjH/mPoxYJU5vHD07jJoVTC8JuUIoLeJojZSDZDQHI5KD2ZwOGU0m3fTYajd2OeDwjWq3zV+0hpN6n1a0p0Ox9T57MueVb8nbt2K9xeHV865lJ/dOCA2+b/6cI8fZifY6K++/9SxLPnuGXrPq2KdKYgqY20Phx75ldB2+H3vtd8J6Df5NgQAvvf4Ks0IR6jUPDYqHoHQTTjmJJ/WMD0AFBq64RwpQnOkZQI4vhk+Nk0PGD0CSvYcNY+dd9miD3v+d+upKfpvwP2qXLSAVa0ZYCYRioKoWqm6gZUfRlouAJ0jxVn85UUxTJRnzk2rOJ17Zg1Q4i0TUja9LPw457eI2t92mdVmnGURnoK2zubZkeaK9fr4hBBJ1/OKdzMk320tKnY3lp5KVzZlLv7kpnCmoyYPZ/XIQQ3fj9PNvXe29Uyb/yjtff0GVpVKveWkUXkKmi0jKQTKhYcZZEQq6AhVUl8TpNPDqCXKUOHkySkEqQneXxuEHj6Z7Rff17se0X39k9m8/EKqvQhpRBAZKZkBXNBNFMxCaiaIbCC2F0AyElkRoKRQthVDT74qafrXcKLYqpBQk49mkIn7MsB+jOZtkxI3U89n3+AvsVPWdjNaYQdhkWDnRnuuwHpy8qy0OnYlH7r4Mfeo39J0ZYr+m9A7iGX0dLOvdh9OveIJdMzOF6dOncv/4T1mm5xCQbppNF7FEevlHJiSixdM/AE6B7jTJ8ibI8sfJJUa+GaHIjDG0vIz99t5vvZ2qz91+KW5XDY6iWoQeazGgJ1FUA1VNoSgWvp6scOyuDikFpqlhmTqWoWOZDqShY6WySBk60tDB0LFSOtJQkYaGZaiYhoppqkhLBdXNsL2PYOA2269XP2w6J7ZArAcrEu05HEwKfWMn2utE/DF5Il8+ez09Zi9j50UWioQFZYI/hhbT74CzODoTNjrx+2959KffmEopgUYPGFsA6eUf1QUOl0G2P45fjZMnI+QbUUoVg/1G7sq222/7r+387LWnqVvwPa4utbgKl1K2bQhIO3GNeBaW4UDGs5CGhswM6jKl/W0wtwwFy9KwUBGaB092HuX9h7DF0JF21I/NemELxDpgJ9rrvNx1zXEUz5lGv1lx9o6lHc2TtvHS1G9rLrrmCQDef+9tjr73YaZbXQg2usDsBRrk5kUZRBXbuhWOOnRMm0QALVk4ly9fuAtvYSPOomW4C2ooLpSYpkassSvheQOINhcy+pzr7MHdZqNj+yDWgp1or/Pxxkv3U/fNm/SeWU95TXrvweyeKov6dGPMRffTrVtvXnj5ed6rizEjWUQ44Ej7CxyCgtwwW1nLOH+3nRkydJs2se+FO6/AoVXi7FKFO28pmp5ESog1F5Ks7Uq0Np+h+55iL+PYrBOLF89n8k8TGH34yRt0v+2D2EDsRHudh9qaZbxw59lUzFlA33kGgwyoLIAJO+bh2W5fTjj9ah585GEuePcrZsWnEWvKR0gQLigpCrG1sYzLjzhsg5zEa+Or916hcvrnuLvU4yxYQuk2QSB9iEy0tgfx6iKc/oGbfWI4mzWzePF83n3tcSwrSLYM0cWop4dRSU9ZheoYDmyYQKwJWyBWQTAQYMJVj7Grby/iZoyvE59w4iP2klJH5KHbz8cz/Sf6zmxmv1A6W+mf/Z1U9d2C0y99hJ+ef4r3onncfNv7JILdEYDihoouQbZJLuX601r/LIL66ko+fOImsvIbcRQvw+2voct2FqapEguU0rhwCyKhfEafc6O9bGTzD+bNncGH7zyLlCF8VjAjBFX0kFVcIFIryi2jgHlKKb/rfalW2mYDpL3EtBLLE+1VZPVakWjv0HMuaAULbVqLH77/jEmv3k6PWVX0XJz+97ugQjC/bynDj76c8T/+xg9aMQvDeRjNCgBqlqQiO8DwZCVXnXZGqw/ML959NbpYjLNLDa78Jeh6+nCZWHM+iboyojX5bLHT4Qzfea9Wbdem8zJr2hQ+/uhFoBmfDFGaqqN7Rgic4q9Q4yUUMl8pZaleREDJJSG9DBiwHfse1DoZaNe0xGQLRAtWJNrTc5gR+o1trjvCjunuIAQDAZ6462xKZs+k3+wE3gQ0+GD2FtnUVwyh1t+bSY6uLA3lYmZ2P+s+ix7eBrY3arnxkv+0qj3ff/ouC375AE+XBhwFS3FnBQBIJd3E6suJVxehuPpy5Hl2pNvmTloIXgJC+K0QJUbtihmBQ/yVfHBxRgiW6cU0qjmkZBYDB+3IwAFbMePH76hZtJR4czS9w94SCKkAKkKoWCQ4/Z6bNsg+2wexDtiJ9jomLz59O+GJH9BnViP716WPvpzdS2NO93KqynZlsl5GZdCPbEjnInLlmPQrqWcXpYnLzx/banbUV1fywZM3k5XbiLO4EndOFSXbW1iWQjRQSmBJH8KNeYw+72Z72Wgz5c/ff+bTT15DEWH8VohSo26FEFyUEQJLCpaIQhaopUzSBtCo5pA03MiwB4/IwxRgpiwMITGFxaSfp/HDpKl/NaLroLdsVQIGXRK+NunTZi8Qf0z8hkCLRHvTuszm2CtscWgvgoEAzz58BdTMo2xhNYPnm+gmLCmGD3cpY26fvfldK6MmkA0N6f0JnlyDvjm17O1Jcc7p5/xrG5bnFzKjVTiyYjgK6nAXLKViZAyAWDiX5kWDiNbm0Xu7Qxl1+P7/uk2bNTPp+6/59vt3cMsg5UYVZUYdEoGFgonAEgomyorv0p8FEgWr5XVB+nrmeynS5SUCS/xV/m/fI5DLX8vLCIGUApeMU5qqo4dZRT9ZzaAWQrBYFLFALeFnfQCNSi5xw0kkqiM0H5ggDIGOiiYVNFXBkBJVCjRLxWOBkBJF6ghLgrRAWkhpABaWMEFYCB30LBdd+xW1ye99sxeIKa9+wi5Ze61ItHfsYDtp2MYiGAjw3LirkDVzyalvJL8uRpc6yV7x9PVmN3w5rILfh+zLVLrSEPBCvUQqkJ2bpH9uNYd08XHMUesfdtxSBJyeOFp2GCWrOXPuQOhv+YVSSSexhnKaarpg0Z1jxto5t9qS+poqXnzuXlQCdDFq6ZdaxBBrMcMzg2+NzGGeWopEoGKhSAtNmjgw0p+xUKz0UL/iMyt9lnLFzy3LqC3kQ8VC/Ue2xL+zXAjmqaX8qA8ioOSQMNzoAQfZRjYSEyksXMLE5RAUOHVyivPYYvhwBgwbsTF+nf8K2wcBPHPFVZx8++rz7tj8e4KBAM89cjWyenZaDOpjdKmVeDNiYCgwr8Bkysi9qc4vp0b3M98soCngSucMVsGfF2egUs2JA3uw9177rbE9WLsItMw5ZFmCZNyHEfFjRnwYzVkkoy4sctjv5EtsX1Qb8sS4m4k0LyNfNtAruYRB5gJ8Ij1ba5Zupqndmesop14pwOPpyhnnXb3Odf/0+XimfvU9MqagSAdoOoaqklIlcdUkJlKYKyXN0qSC29JxWSpGIoY0EiAspJZAOMDftQhfbgFGIkHPPoMYvsOurfnr2OjYTmqbjUowEOC5R69BVs0ip6GRvLq0GGTFoRmTL/bbh0Vdyqlx+KgXWTSl3MTiDoyYALNFRbogLzfCYCo5Y7uhjNxhp3+09bflIG8cPSuMkhVC8wZXIQIKyVg2RtSPGfFjNHtJRtyYSg67HHoq3fv23wi/nc2bD95+kdlzfsJPgO7JSgYaCygRjQCkpMpMpYJZegXVajGGyOXQo86iomLVSaYaqiv53/PPE6kJoVhOhOLAUlUMTZBQLWJKioRYKfGgBLfUcVsaDlNBMyTCTCFlClNLUtizC3scdfxmtd/JFgibNiMYCPDCY9diVM4ip6GB/LoYnlqDn/fcn0VFpdQ4/BkRcBGNOzCi4m9ZTqUA1Q0uVwqfI06+iFBshKkQCY47cDS9+/SlKRDgo2fuwYrWrIcI5Pw1E4i4sJRcdj70FFsENiJpp+0ruESQslQVW6QW0VtWomSWbRZSzDStB0v0EqL42WnHQ1b7NP7hM49TNXUJivCSdKqENYOYSGKttASkSRWPpeO0FHQDVMMCK4klkqg+je0P2LtTLO1sTGyBsGkVlouBWTULtbaJhu47UplXSrXDTwPezExAX6MI+B0x8kWUYqOZbkqKYw44mN59+v6tndlTJ/P7l/fgKKpebxHY7agz7fOI24FgIMBzT98BZgPFRh19U4vpby3CldnYVS99/Kn1ZL6jK03kUVo6gKOOX3VAwS9ffcbkD79ClV4Mh4OwbtCsJlZczzadeA0NzbBQTANLJrB0g9L+3dlp9OGb1dN/a2ALhM16EwwEePzeK2kSPuqyi6jR/dThJZTMiEBsFSLgAbcriU+PUyCiFBshuikGR69CBFZFUyDA+McuwT/od1yeIPGoDyOSu0IEEhEX0haBDsFzj99BQ2AR+bKBnsmlbGnOJ0ekN6BEpZM/le7McZRTrxagOYo57sSLVzlwL10wj48fewol6cbSnUR0SUiNr5gZuC0dn+HAkTCxZISSLcs54OQzNmpfN3XsfRA268yJd9/Pr1ZF2idgHJgWgczDm1RAc4PLlcSfHadARCgyQvRQTY499PB/lcfomZvOIb//VIq3rSQe8VP9067sf9Zd9p6Cdqa+popXX3wAwwjik02UpWoYYC7kROoBMKVglijnS8dQKrVikuRw8JjT2LZ3f1ZOfh4MBHjn4QcxGyyk5iHmgJCWIOVygMtElwn8hpPSqAuMGM4ijeOvtDcatidtKhBCiH2A+wEVeFJKeftK1yuA54CcTJkrpJTjV7o+HbheSnlXW9q6ufPEozfzYqI7i+p6I1zg9Sbx+WIUEKE41Uwvh+So0WNaPZndy/dch7twEuUjZ2EYThqmbs9Wu19C3wPtg+o3JsFAgDdfe4RgsJIsQhQb9XQ3quhjLeM88dfyzmIK+VPrwXh9B8LkMGTIbuyxzyEMWEWd7z36ELWza1AULwmHQkhLEVcs8IMiY/hNF0VxJ0oqgeWMceiF55FvR4t1KNpMIIQQKvAwMApYCkwSQrwvpZzeotjVwOtSykeEEAOA8UD3FtfvAT5uKxtt0txwxTm8lrcX0UadvKIo1/YyGX3wUW3a5vefvkvV/Jcp2HIKQliEFg5G13bniAvPb9N2beCt155k4cKpuEUzhUYDFalq+lhLOVWEV5Spk37mqF1537UD9Wo+cZnFloN2Yu/9x1Cxijp/+nw8Uz7+DlVmkXLqhHWDsJIAnwrEyTad5CYd6EkwlAjbH7o3g0fustH6bLNhtOUMYltgrpRyPoAQ4lXgYNIzguVIYPkecT9QufyCEGI0sACItKGNmzWfjX+NzyZP503P/pgBGFxSzQsnHNqmyzr11ZV8/tJV+Af8Sm7fKM01PQktHMwJV97dZm1urkyc8Ak//vgJumgmzwxQnqqht7mUMZmwUkjvM5ijlPG1cyg1aj5RmU1xUQ+OO2UshcDIVdS7dME8xj/2FGrCjelwEnVYBJUEMtsBJPFYkG3o5CbAlBHKh/Zk3+NO2VjdtmlF2lIgugJLWnxeCmy3UpnrgU+FEOcDXmBPACFEFnA56dnHpatrQAhxBnAGQEXFqp5rbFbH3f85lMklu/N9bFtQBfvlTeORC9v2PIIXbj6DnEFTKNy6nmiokPpfR3D81Y+3aZubA3+lhw6SYzVRmqqjl7mM7WQ1IzPO3oTUmCdK+U3vy3i9kGZ8OJwFHHfixQzNzWVtC3rP33IjRqNGxKUR1OIYK/kNyuIupBHDVaxz3BVXtX2nbTYK7e2kPhp4Vkp5txBiBPCCEGIQaeG4V0oZFkKs9mYp5ePA45COYtoI9nZ6fv35K76//3Le2/ZCFlfloPsszsiex2Xnt504PH/bWHzd/6B05AKScS91k3dkz+Nuo2C0vd68PgQDAV55/gGi8VqyCVFi1NEjVUUvuYwLM+GklhQsEsXMU7vyrb4VQcWPxM+Bh57MgN79V+krWB2v33cX4cUJIh6dBs2C7CR+U6E45kJJxZGeBIecf4btN9iEaUuBWAaUt/hclvmuJacC+wBIKX8QQriAAtIzjcOEEHeQdmBbQoi4lPKhNrR3k+fuq49BrVN5fMhVRKt08ouiPLhdF0bucFGbtPfBcw+SMr6kZPifSKkQmLUNJb2OYd9LR7dJe5siwUCApx6/mhGJyQw0F3KWiK64ViXzmKt2ZYq+Cw1qHimZxTbDdmeXPQ6kxwa29+Ezj1MztYa420m9FkP6JD5TpTzsQjqbOe2W61ulXzadg7YUiElAHyFED9LCcBRwzEplFgN7AM8KIfoDLqBOSrkip4IQ4nogbIvDhjN77lS+ufk0anruy5ulwzEDMKSkiudPGNMm/obZUyfz+1d34+v3G24tQfOyLYg3Dufoi65v9bY2VZYLw6j494yVC1hMIZ86h1OrFRCTWXQt3YKjjj+HklZo6+u3X2Pet7NIuN3U61Esn8RrmZRFXVgiyBFXXmhvPttMaTOBkFIaQojzgE9Ih7A+LaWcJoS4EfhFSvk+cAnwhBDiYtIO65PkprJzr4Nw302n0f/LiXx54Dl839ADoQoOyJvOwxeu1rWzwTQFAnz02KXkDPqN/EFBIo2lVM/ckpOuHtfqbW2qrEoYHnIfzqgDzuewgUNarZ2fPh/P1I9+xnB5qXPEMP0WbitJacyNZTVx5P/ZomBj76TeZAkGArx8+UH0ndTE9SddwZKMv+Ec/3wuPvfCVm/vmZvPIX+LqXjz0hvdmv7c2t7oth4EAwGefvz/2CM+kcEZYXjfsyuj9j+ffq0kDHP+mMyEZ97BdPqod8ZJCROn1CiIOyAV5ND/nGv7EzZD7J3Umxnj7hxL+Wf/w+q2HecdPZpYlUZBcYRxI7qz7fYHtmpbr9x3Pa68SZSPmIlhOGn8c3uG7GZvdFtXWgrDxctnDJ7DGbX/+ZzXCsKwdME8xj/4DOh+6l0Jkn4Vh4xTmHBBMsTeZx5Bt37r47q22ZywBWITIhgI8PzVYxj5XRWvHHYUb1nDsAKwVUkl7114equ2tXyjW/7Av290O/wCe6PbupAWhmvYPf49F8v5LGlFYWioruTtOx8CNYcGV4q4T0OTMQoTbtREkpHH2hlNbdYNWyA2EZ599EZ8H77OnnNNbjzzPCbWdUfoggPzpvFQK+5vqK+u5PMXr8Q/4Ddy+0bsjW7rSVsJQzAQ4LVb70Qhh0a3QTTbgSpjFCQ9FMRTDNhzK0buf1Ar9sRmc8AWiE2A+y7ahxHfLCJiapx+9jUsrfLj9Fuc7V/Axee0njikN7r9QeHQuvRGt8nb2xvd1pHlzuc94j9wsZyXFgb3Yey695mct9XKae3WnSeuuAph+mlyW4S9DoSMUZhykx8zqNi2G6OOPrEVe2GzuWELRCfmvTefIP7yg+w9PcX43bfniW6jiVdpFBaHeXKX3gwZ2jr+hudvuySz0W2+vdFtPWkpDGPlPJZS8K+F4alrroGoh5BHEHSlRSHf8JAb1sjt4+PQsy9o5V7YbK7YAtFJufuyg9luwmxym+HxE47h3dRQrCbYussy3r2odfLlf/jCIySTn9ob3TaAYCDAU09cw+6xia0iDMtTXTS7VQKaCtkJ8gwPFSEdV4nGMZf9pw16YbO5YwtEJ+Pbrz9g4aPXst/vcWpz4cYzzufHum4IHUbnTeP+i/79QDF76mSmfHU32f1+w5XZ6BarH8YxY29ohR5s2qxSGDyHsete6ycMc/6YzNfPvo2iZhNySYJqOtVFjuGmotmJ4k9w0s22KNi0LbZAdCLuuvIIhk2YyrBG+HLrPF7a/lwqM/6G83MWc/7Z/27AaAoE+OjR9Ea3vMxGt6qZW3KyvdFtraSXkq5ht/gPjJVzWbYBwvDsjddjNWkkXE4a9Tgpn4aQcfIMN+VRF7jCnHrL5W3bERubFtgC0QmYMW0yP9x+Jvv8EiaUBa+OGcVrvj2IV2kUdQnzxM7/3t+wfKNbl+0qiUf9VP+8K/ufeRc5h9kb3dbEqoThYc9h7LIOwvDHxAn8+ObnKFo2IadFSAV8Bh5LoTDuRElG6LZtD9vRbNNu2ALRwbn7uhMZ/PXPjKiBqVto/LD90bwbH4LVBENLlvLOhWf+q/pXnOg2InOi25/bs9Vul9D3AHuj25pY7nzeNf4TY+WcvwnDuWsQhqevuRoibuIunUY9juFTUWSMPMNNRdSB6QhzxH/sNBc2HQNbIDootTXLePeqw9nzxwAJB3y6dzkTux/Cj3UVCB0OzpvOA/9if8OED9+gbukb/zzRzd7otkb+KQz5jPOMYee9zlqlMEz86H2mff4L6FkEXSZhVQNfiixLoTjuQiTD9N1jMDsfcFg79MbGZs3YAtEBeeC28+j9xZfstFQys6fCol0O5DXnEKqqfbj8JhfkLeXcMzdMHOqrK/ni5Svw9f/NPtFtPVidMGy366mcs+0Ofyv3+h13oiaziLk0GrUYpl9BlXHyUm7y4hZ4Ypxy8/Xt1xkbm3XEFogORDAQ4MUrR7PjxFoAvti9GGXIYTwSqCBRrdKlSzNP7zWQAQM2bEfsC7ecTu6gKRRs1UA0WEjdryM54erHWrMLmxzLfQy7xH9crTB89sZLLJo4B8vhIeg0iDgd4EziMxVKYm4so5mhB+7AsN1GtXNvbGzWDzubawfh+Sduxv/2K/RdYDG/XLBgjz2Yr5bzTmQgVhKGFy/lzQ30Nzx7y0Xk9J5KduFCEnEvoWlD2fP4WymwM3eukmAgwJOP30iurGF4YjpbygVUyjze9e7GdrueSu8+A3jttjtRzWyiLpWAFsMSEk2q5KdcOOJJhC/FKddf395dsbFZK2vK5moLRAfg2UdvpPzFVygIwA8j8jj0ltc555UPmFRbjuKAg73TuG8DNkK9/fidoH9Hdvk0LEujee5WlG1xPNvvvn8b9KJz8/H7rzBr5jd0Ty1mRPJPikUTAFNET753b41L74W12MByegg4ksSU9BGfftNFdlzBMkPseMz+dhI8m06Hne67A/P0uOvo/vLr5DXB5/v2Yd9TbuGgV76hurocV47JhflLOef09ROHKT9MYM7kR8nu8xuqatC8pD9GZASHn2sfJr+cYCDAs0/ejtuqYlBiDnuYs9hXmISkmx/0Qcx29CAW8ZAfLsFIKSxRY0i/RJdx8pMuCuIWjiLB8Vde0d5dsbFpM2yBaEeeHHctvV56g9wgfLFPH9z99uGQ9xeQaPLRpUuIp/catF7+hqZAgPFPjCVn4G/k9m8mXF9O4+wtOfnqB9uwF52HSd9/zbffvUlXcxnbJadxITUAzKYrb7h2p0qUoNV4cbnyCacSJIRBNCtOjummLOrCNO3zE2w2L2yBaCf+Lg59WVa8A+8s65b2N3RZwpsXnbVe9T1301nkDfyD4mE1xMJ5VP24G8dd9WQbWd95ePT+G7ASi9giMZ/tjekMFwniUucndQAfO3ekOZZNXqSUsCkJq3Hwg0smyUs4URMmvm5ejrio9Y9ntbFZE4uWzOeHHz9jae08QokQCeKkRAJDSWIqSSySGCKFKZKYIoXXzGfc+eNb3Q5bINqBJx68it6vvENuM3yxb19+LR3F93U9UBwwJnca96xHPqXn7rwCf+lvlO0wl1TSTf2UHRh5yLWUH9S7DXvQcZk3dwbvvfUIRbKKbeIzOYvFACylgI+cI1iidkWpz8KpF9JgxjGEScwTJ890U97swlRCjLnkDPvoTZsNoq62iu9+/IQFy+YQjDUSF3EMkcAQSQyRwFoxqCcxhIEhDFLCIKlYJIRFTJHEFIgqSrrCrMxrFQgp8VoSt4Tucb1N+mMLxEbmsfuuoN/r7+FvhvF79OLTssNZWJWL029xYd4Szj1z3cThs9eeJhT6iNKt/0BKQdPcrfHlHcyRFx/fxj3oeLz47P001k6jd3IhI1N/MlZESEmVyWpfnnAeRFMqD0djLkaWk2YlAV7wWimK7HQWNuvJDz98xXs/Pk9QqyGs1hNW4ySUFgO7EEghwEP6tRo8loXHApclcFoKDkvBKx3oUkOVevqFA9VyoEoHmnSgSydO6SLHU0DP8i0YOXx3CotK2rS/tkBsRB677wr6vfYe/jC8v/9I3izYn+YqJ3lFUR4d2W2dzoteOHsGP4+/Fd8Wk8kpjBOq7Eu4emuOv/TWjdCDjkF6b8JN5MhqtkrM5hhrLoqQ1Ekf3ziGMF+vwApk41CKaDTTIajxbIU8QyU37MB0hTniMjudhc2aWbRkPk+/cSeNWiVhtY4GvZklDomRJwDINywKUzpuQ6dQ6qhSQ5WOvw3umnSiWw6cuPC78qgo6cW2w3ajW3nPdu7dumELxEbisXv/wxavf0B2BN4YM5rXtR1I1Qu2KKnltRNGk7OWwaopEODDRy8ld9Dv5A9uIhIooWbSlpx4zSMbqQfty8phqGNbhKE+596XeqsQvTGfhNSIpVLgBJ9pUWpvVLNZB4KBAE+9eheLk7MJq7U06U0scRhE89NLPV7LojyhMyySgzdVRDdXPy4+66Z2trrtsQViI/DI3Zcx4M0PyY7AU8eeyEeRwRCX7F40l2cuvHCt9z9787nk9vuTku2WEo/6qJm0C/udcTc5YzbtJ+AH7rwCp6xiUHwOe5gz/xGGGo/4cBpFBKwYUoDDq5CbdFAYlyj5Fiddb4eg2qyaV994jF+WfkOzVktIa6DKkaDBo4AHNCmpSAoGRnPwmoUUmGWcc+z/tflyTkfEFog2pqU43HLKhfxSU47ikhztm8atY9fsb3j1gZtw5vxI2YhZmIZOw7TtGDjyAgYesP1Gsn7jM+GLD5j667vsEf2ZCzIO5pZhqCKQQwInScMAwAGURV1YVoi9zjjODkG1+Qe/TPqOt759mqBeTVitp1aPsswhIC99vWtSUpHwMiBagD/VhTE7ncKw4Tu2r9EdBFsg2pBxd45l0Fsfk0hqnH/KlSyr9uPONbispI5TT1i9OHz/6btUzXuZvP6ZTKuLBqGyG0ecv/bZRmfl4fuuJSc2g/3j37OLiDCdCh5xH0pzzI9udiGYiAPgcuoUJBwoCZO8vrn2+cs2f6OutopxL91CvbqUiFpHgx5isUNi5Kf9BnmGRWnSSbd4PtlGEcPKduaoE/9dyvxNGVsg2oiH77yILd/6hBl9BnP/lkcTrdYp6hLm2b0GMGDAwau8p766ks9fugp//8nk9ovQXNMjk2n1no1s/cYh7Wy+li2T0zjLmIxE8KU2lKn6AMJNfiQaihR4LIXyqBNTbebIK23nss1fPPj4DcyPTSes1dCkNrHEmSKSl/YbeCyL8qTGNuEcsswiKhx9OfWoS+1/P+uBLRBtwEP/vZAhb3/Kp6MO5mXnThgBGFxSzQcXnrrK8k2BAB88chk5A6ZSuHU90VAh9ZO35/irH9/Ilm8cJk74hMk/vcEe0Z8Yy2ICMotX3KOojJcTN/xYpqRI8eIIRui90xbseuiR7W2yTQfhwcdvYGZiEgGtiipHnHqnAs6036A8Kegf9ZNlFpJnlHLK4Zd1mmihjootEK3MQ7efz5B3PufFw07lk9AASMDe+TN5/MKx/yjbFAjwwbj/4O8/ndLtq0nGvdRN3pE9j7uNgtGb3katR++/gazYn+wfm8hIEWYGFTzkPpxYUxER4UCTCiVxF0ILctqtG34Yks2mxa0PXMwSMZ1qRxXznBbSKShOWZQlvGwRLcBvFHPw9icwYsRu7W3qJoctEK3IQ7efz1Zvf84dx45lSnUpqkdygnca16/CGf3CzWekhWFEFcmEh4apI+i37ense+ku7WB525FOnX0tWyanc5oxGYHkS20oU/SBJJpySaHg0R2UNwsK+mRz8FnntbfJNu1MMBDgzmeuoNYxl0pHLYv86e+7J2BEuAtdUn0Ye9Kt9lLRRsAWiFbigdvOo+zTn7nkmOuoqcrGm5fi+p5xjjjs7+Lw/E1nktN/OqUjK0km3DT8uT09tjqRfS/cq50sbxuWLyPtHv2JS1hMk/TymmtPKpNlpIw8kqZJrubGG4yzy8n70GewfQb25kxdbRV3vXgl9c75LHY2UJ2vIKSkT0Jlx+YSustBXH7+Xe1t5maHLRCtwAO3nYe5JMJV+/wf8WqN0i5BXj50JN0ruq8o89zNZ5HTbwZdd1hKKummcdp2lA88ln0v2LTOZnj0/hvwxqaxf+x7RoowsyjjIfdhxANFRHABkqKkCy0Z4IirbYfz5syMGVN48n//pcGxkIWuIA35CpqU9I076BsrY5Bne84+8//a28zNmjYVCCHEPsD9gAo8KaW8faXrFcBzQE6mzBVSyvFCiFHA7aTD3JPAZVLKL9vS1g3lgVvPZVkqnzeK98Vsgm26LOXti/4Km3vuprPJ6TeDspFLSCVdNE7fltK+R7LP+aPbzebWJh2NdB0DU9M5LfUrAslX2lCm6AMwG/OJItCdKl1jThRPlFNus/0Lmytffvk+7099gQZ9EXNdEcJ5Cm7Lok/czeBIBTuV78/hJ606mMNm49NmAiGEUIGHgVHAUmCSEOJ9KeX0FsWuBl6XUj4ihBgAjAe6A/XAgVLKSiHEIOAToGtb2bqhPHDL2fzu7MuX0b6gCA7Im87DmdTQz950Nrl9Z1K2w2JSKSeNM4ZT1G0M+5x3eDtb3XpM+v5rfvruJXaL/cxYFhKUHl5z7cGyRBkiVUTETOJ1OakIScpHdGPU4ce2t8k27cBLrz7Mj7Wf06AvZrYrQSJH4DMt+sazyEt255BhJ7Hzjvu2t5k2q6AtZxDbAnOllPMBhBCvAgcDLQVCAr7Mz36gEkBK+VuLMtMAtxDCKaVMtKG968V/rzqJr4t3ZnpVMVqW5NTsGVx14aU8c9M55PadRfkOCzFSDgIzh+EvOoDDz910sqw+/tDNuMJT2T/2PcNFM7Mp42HPYSQaC4nixRAm+ZZGbnOCfS44grIevdrbZJuNzIOP38CMxCTq9aXMcRkYfkGBYTE4mkNesgdnHnQVfXrZu947Om0pEF2BJS0+LwW2W6nM9cCnQojzAS+w5yrqGQNMXpU4CCHOAM4AqKioaAWT141b/+8M3irYn4YqD9kFSW7vr1I/eQHvvronFTssSAvDrG3w5+3DYeecstHsakvS0UjXMzA1nZNTv6Ji8ZW2Fb/rAxENRYSkRHEKihMuhBngjDuvb2+TbTYyt9x/EUuUGdS0CEctTUq2CedTmOzF2ONu2SzzGXVm2ttJfTTwrJTybiHECOAFIcQgKaUFIIQYCPwXWGWIj5TyceBxgGHDhsmNYfC111/Gq1kHkKxV6VYS4KTmHzBCsynfYT6GoROYvQ0u724cdvbZG8OcNmfS91/z4/cvsVv0Zy5hISHp4Q3XbixNluNIdqHZjOP0qJRHdZz5BsfdYPsXNheCgQB3Pns5tfpcljlrWZyTTmfRY3k4arIPY0+2w1E7M20pEMuA8hafyzLfteRUYB8AKeUPQggXUADUCiHKgHeAE6SU89rQznVm7K038Q67YjXDlc4P6FI4n6xB8zBNncDcoTidO3PYWee3t5mtwhPjbsYRmsr+sYkMFyHm0JVxnjHEGwtISj9xJYUGlIc0Bu03lO323K+9TbbZCKTDUa+g3rkgHY6a1yIcNVRKdwba4aibEG0pEJOAPkKIHqSF4SjgmJXKLAb2AJ4VQvQHXECdECIH+Ih0VNP3bWjjOnPCPQ8yITKUS5TxlA+cS3aXuViWRnDeVijKjhx2xsXtbeK/5sWn76G+bg79UnM5KbOMNEEbwmR9S7T6QkJSYLrSh6TkN8c54toz7afDTZBgIMD7/3uRGVW/E1GbiKshEiJMRI1Ro6cI2OGomw1tJhBSSkMIcR7pCCQVeFpKOU0IcSPwi5TyfeAS4AkhxMWkHdYnSSll5r7ewLVCiGszVe4lpaxtK3tXR1MgwGHPv8+BjbM4pP94skvmYFkqoflDQI5gzBmdc0mlvqaKF5+7F002UGFUsmVqPsdRDUBIunnTtStLjHK88a40m1FUr6A44UYQ4PTbr29f423+NTNmTOGdz56lwawhroVIiGZiaoRmNUGdZhFWFcj/q3yuYZFvqHRPZLFVpKsdjrqZIKTcKEv3bc6wYcPkL7/80qp1fjPhC375/B169J1PdulsLEuhedFArMRwDj/3qlZtq62ZOOETfvjxQ3JkI71SSxlizCVXhAFokNlM0Xozz1FO0PKjN3owXXmE1DhuS6cgqpLTw8WYcy9q307YrBcvvfow0yon/2MWENBS1GsCU4gVZXUpKUqB39Txmh6cMhuX6SfLzGH7LXZj//2Oasee2LQlQohfpZTDVnWtvZ3UHZZHrj2XLr0XMnjXWViWQmjRIJLhbTjqgmva27R14rnH7yDQtIBis5b+yQUMsxYyUpgAzKWUCY6tWKqVEE1lIUN+cDmIGSkQgBdyTCgPqYw4ZncGDBvRvp2xWSUzZkzh3c+epcGsJaYF12kWkGeolCS99Ih7cVk+XIafEnc5x485z44wsvkHtkCsxAt3XoG36E967zwTEIQWDWTZ/CLOv/mJ9jZttbz77JPMXfoHbkeEilQlW6bmcSI1AMSlzp9KD1537UGtUkgs5iSFH1KgJ1Xclo5TV9CTggJTBdPAFFGOtNNgtDszZkzhoy9foi5RS1xtJqE2E1eaiSgtZgH+Vc8C+sc8OK1snKaPbCvXngXYbBC2QGR48e6r8eRPocvWMwFoXjqAJXPKsPQ4F7eTOAQDAb5561WqZy9BpFSEcIKq0aQEIbeZHAL0SS5mN3Muo0UUDKiTPqZoffjUsT0h048I+fDJHJRmE7+VIpskOJN0HzrQ3tncjvzww1d8Nel9mox64lqYpBIhKSLElTgRNUlQtWhUBTJLQNZf9+WYaV/A8lmA0/LhMXwUu8o57MDT7PMPbFqVzd4Hcd1Np1OV2yGiaG1WQkgVFR1VOlAtB5p04JBOXIqHXE8BfbsPZPvhe3S4mc57H73E73MmEpIBEmqElBIhoUSIiwRhNUWTZhFUlX/cl2Va5JqCbFPHbTlxWB50y4vTzMIrfQzrtzMH72+Luk3rYvsg1kDcneKH7Lr2NsNmQ6h6E+W96/FYEo8ElyVwWAoOqaJJFV1qqFLPvBwoUkeTDlTpRLccOHDi0/2UFnZj2+G7rTX1QzAQ4O2PnmVe7UyiIkRCC5MUEZJKlJgSp1k1CGiSiKLASpqVY1rkGArZlk5BzIVTenBYWTitLHzksk3fHewlIJsOx2YvELmOLRk9K595sog+3np8RgTLqaIoElWYaNJAI4VDJnHKJC6ZwG0l8MgEHitGlozjJUaWjJFNFEfGEbwmDKkQxk1YuAnjJiJcRIQbFZMtzfn4RAyAWpnD71pvFjjLaJa57LP3MQzaatu2/pV0GH6Z9B2/Tf+eumA1USNMggSGksQUSczl76QwRfqVEgYpYZJQTIIiRVyRxBSIKv98Wv8bCeC7R3F9I/FYEpclcEmBLhV0SyWlmIRUg0ZVkFDE3xy/QkryTEmOqZJrOClJunFIDw4zC6eZRZ6Wz67DD7JPO7PplGz2AhFOmbzk2J3ds2az96sf8vPBO3LJleM2uL5ff/6eGX/+RFOgjqSZQMoUAgsNA02k0GUqLTRWooXYxPHLCAoWnzmHs0wrQehFnHDSJezVwZZPNibDhu/IsOE7/ut6Fi2Zz8+/fMWS6vkEY40kRJyUSGCIjMiIVIv3FAYGhjBIKibNagrH/7d3dyFS1XEYx79PbtruKtmLQu6Kay+UFqkVYllS2UVvaBeGvSjRtZVGUBlJ0LX0ciFlWGG4VGQKEVGSheCFrqaWqQViqWuGGmkZ5Nv+ujgndiaOtebkf5rzfK5mzswenvmzM895mfmfEEOPtTD8SDP9e1rp39NKc88ghgy4iDtvmc6oUWNq8GrN6k/pz0HMnzeDjjU7GXbwAF1TbmTOvPr9tpKZWa393TmIf9j3bnyDLmhngH5i3dRJLgczswql34MwMysz70GYmdkpc0GYmVkhF4SZmRVyQZiZWSEXhJmZFXJBmJlZIReEmZkVckGYmVmhhvmhnKT9wM7TWMWFwIEaxfm/81hU83j08lhUa4TxGBERQ4oeaJiCOF2S1p/s14Rl47Go5vHo5bGo1ujj4UNMZmZWyAVhZmaFXBC9XksdoI54LKp5PHp5LKo19Hj4HISZmRXyHoSZmRVyQZiZWaHSF4Sk2yV9K2m7pKdT50lJ0nBJn0vaKmmLpNmpM6UmqZ+kjZI+TJ0lNUmDJS2V9I2kbZKuT50pJUmP5++TryW9Lemc1JlqrdQFIakfsAC4AxgN3C9pdNpUSR0HnoiI0cAEYFbJxwNgNrAtdYg68TLwcURcAYyhxOMiqQ14DLguIq4C+gH3pU1Ve6UuCGA8sD0idkTEUeAdYGriTMlExN6I2JDf/pXsA6Atbap0JLUDdwGLUmdJTdK5wCTgdYCIOBoRB5OGSq8JaJbUBLQAPyTOU3NlL4g2YHfF/W5K/IFYSVIHMA5YmzhKSi8BTwI9iXPUg5HAfuDN/JDbIkmtqUOlEhF7gPnALmAvcCgiVqRNVXtlLwgrIGkg8D4wJyJ+SZ0nBUl3A/si4ovUWepEE3AN8EpEjAN+A0p7zk7SeWRHG0YCw4BWSTPSpqq9shfEHmB4xf32fFlpSTqbrBw6I2JZ6jwJTQSmSPqe7NDjrZKWpI2UVDfQHRF/7lEuJSuMsroN+C4i9kfEMWAZcEPiTDVX9oJYB1wmaaSk/mQnmT5InCkZSSI7xrwtIl5InSeliJgbEe0R0UH2f/FZRDTcFmJfRcSPwG5Jl+eLJgNbE0ZKbRcwQVJL/r6ZTAOetG9KHSCliDgu6RHgE7JvIbwREVsSx0ppIjAT2CxpU77smYj4KF0kqyOPAp35xtQO4OHEeZKJiLWSlgIbyL79t5EGnHbDU22YmVmhsh9iMjOzk3BBmJlZIReEmZkVckGYmVkhF4SZmRVyQZjVAUk3e8ZYqzcuCDMzK+SCMDsFkmZI6pK0SdLC/HoRhyW9mF8bYKWkIflzx0paI+krScvz+XuQdKmkTyV9KWmDpEvy1Q+suN5CZ/4LXbNkXBBmfSRpFDAdmBgRY4ETwINAK7A+Iq4EVgHP5X/yFvBURFwNbK5Y3gksiIgxZPP37M2XjwPmkF2b5GKyX7abJVPqqTbMTtFk4FpgXb5x3wzsI5sO/N38OUuAZfn1EwZHxKp8+WLgPUmDgLaIWA4QEb8D5Ovrioju/P4moANY/Z+/KrOTcEGY9Z2AxRExt2qhNO8vz/u389ccqbh9Ar8/LTEfYjLru5XANElDASSdL2kE2ftoWv6cB4DVEXEI+FnSTfnymcCq/Ep93ZLuydcxQFLLmXwRZn3lLRSzPoqIrZKeBVZIOgs4Bswiu3jO+PyxfWTnKQAeAl7NC6By9tOZwEJJz+fruPcMvgyzPvNsrmanSdLhiBiYOodZrfkQk5mZFfIehJmZFfIehJmZFXJBmJlZIReEmZkVckGYmVkhF4SZmRX6AzbDkGdgfPz+AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1374,7 +1374,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1386,7 +1386,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1405,7 +1405,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Get best model and measure test accuracy"
+    "### Get best model and measure test accuracy"
    ]
   },
   {
@@ -1417,7 +1417,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test accuracy score: 0.9865\n"
+      "Test accuracy score: 0.853\n"
      ]
     }
    ],
@@ -1435,7 +1435,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 8. Additional note : model selection as an hyperparameter"
+    "### Additional note : model selection as an hyperparameter"
    ]
   },
   {
@@ -1456,7 +1456,7 @@
     "            'criterion': Choice(['gini', 'entropy']),\n",
     "            'splitter': Choice(['best', 'random']),\n",
     "            'min_samples_leaf': RandInt(2, 5),\n",
-    "            'min_samples_split': RandInt(1, 3)\n",
+    "            'min_samples_split': RandInt(2, 4)\n",
     "        })).set_name('DecisionTreeClassifier'),\n",
     "    Pipeline([\n",
     "            OutputTransformerWrapper(NumpyRavel()),\n",
@@ -1509,9 +1509,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "local-venv",
    "language": "python",
-   "name": "python3"
+   "name": "local-venv"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/examples/auto_ml/Introduction to Time Series Processing.ipynb b/examples/auto_ml/Introduction to Time Series Processing.ipynb
new file mode 100644
index 00000000..8df1b92b
--- /dev/null
+++ b/examples/auto_ml/Introduction to Time Series Processing.ipynb
@@ -0,0 +1,1148 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Time Series Processing Example with Neuraxle\n",
+    "The goal of the present tutorial is to show how usage of the Neuraxle framework can make a difference in term of clean pipeline design through an example of time series processing.\n",
+    "\n",
+    "\n",
+    "## The Dataset\n",
+    "\n",
+    "It'll be downloaded automatically for you in the code below. \n",
+    "\n",
+    "We're using a Human Activity Recognition (HAR) dataset captured using smartphones. The [dataset](https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones) can be found on the UCI Machine Learning Repository. \n",
+    "\n",
+    "### The task\n",
+    "\n",
+    "Classify the type of movement amongst six categories from the phones' sensor data:\n",
+    "- WALKING,\n",
+    "- WALKING_UPSTAIRS,\n",
+    "- WALKING_DOWNSTAIRS,\n",
+    "- SITTING,\n",
+    "- STANDING,\n",
+    "- LAYING.\n",
+    "\n",
+    "### Video dataset overview\n",
+    "\n",
+    "Follow this link to see a video of the 6 activities recorded in the experiment with one of the participants:\n",
+    "\n",
+    "<p align=\"center\">\n",
+    "  <a href=\"http://www.youtube.com/watch?feature=player_embedded&v=XOEN9W05_4A\n",
+    "\" target=\"_blank\"><img src=\"http://img.youtube.com/vi/XOEN9W05_4A/0.jpg\" \n",
+    "alt=\"Video of the experiment\" width=\"400\" height=\"300\" border=\"10\" /></a>\n",
+    "  <a href=\"https://youtu.be/XOEN9W05_4A\"><center>[Watch video]</center></a>\n",
+    "</p>\n",
+    "\n",
+    "### Details about the input data\n",
+    "\n",
+    "The dataset's description goes like this:\n",
+    "\n",
+    "> The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. \n",
+    "\n",
+    "Reference: \n",
+    "> Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.\n",
+    "\n",
+    "That said, I will use the almost raw data: only the gravity effect has been filtered out of the accelerometer  as a preprocessing step for another 3D feature as an input to help learning. If you'd ever want to extract the gravity by yourself, you could use the following [Butterworth Low-Pass Filter (LPF)](https://github.com/guillaume-chevalier/filtering-stft-and-laplace-transform) and edit it to have the right cutoff frequency of 0.3 Hz which is a good frequency for activity recognition from body sensors.\n",
+    "\n",
+    "Here is how the 3D data cube looks like. So we'll have a train and a test data cube, and might create validation data cubes as well: \n",
+    "\n",
+    "![](time-series-data.jpg)\n",
+    "\n",
+    "So we have 3D data of shape `[batch_size, time_steps, features]`. If this and the above is still unclear to you, you may want to [learn more on the 3D shape of time series data](https://www.quora.com/What-do-samples-features-time-steps-mean-in-LSTM/answers/79038267).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 0 - Loading the Dataset\n",
+    "This first part downloads and load the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "Downloading done.\n",
+      "\n",
+      "Extracting...\n",
+      "Extracting successfully done to /home/gui/Documents/Neuraxio/Marketing/talks/AiHubspot 2021/data/UCI HAR Dataset.\n",
+      "\n",
+      "Dataset is now located at: data/UCI HAR Dataset/\n"
+     ]
+    }
+   ],
+   "source": [
+    "import urllib\n",
+    "import os\n",
+    "from subprocess import call\n",
+    "\n",
+    "def download_dataset_if_needed():\n",
+    "    print(\"Downloading...\")\n",
+    "    if not os.path.exists(\"UCI HAR Dataset.zip\"):\n",
+    "        call(\n",
+    "            'wget \"https://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI HAR Dataset.zip\"',\n",
+    "            shell=True\n",
+    "        )\n",
+    "        print(\"Downloading done.\\n\")\n",
+    "    else:\n",
+    "        print(\"Dataset already downloaded. Did not download twice.\\n\")\n",
+    "\n",
+    "\n",
+    "    print(\"Extracting...\")\n",
+    "    extract_directory = os.path.abspath(\"UCI HAR Dataset\")\n",
+    "    if not os.path.exists(extract_directory):\n",
+    "        call(\n",
+    "            'unzip -nq \"UCI HAR Dataset.zip\"',\n",
+    "            shell=True\n",
+    "        )\n",
+    "        print(\"Extracting successfully done to {}.\".format(extract_directory))\n",
+    "    else:\n",
+    "        print(\"Dataset already extracted. Did not extract twice.\\n\")\n",
+    "\n",
+    "DATA_PATH = \"data/\"\n",
+    "if not os.path.exists(DATA_PATH): os.mkdir(DATA_PATH)\n",
+    "os.chdir(DATA_PATH)\n",
+    "download_dataset_if_needed()\n",
+    "os.chdir(\"..\")\n",
+    "DATASET_PATH = DATA_PATH + \"UCI HAR Dataset/\"\n",
+    "print(\"\\n\" + \"Dataset is now located at: \" + DATASET_PATH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    import neuraxle\n",
+    "    assert neuraxle.__version__ == '0.5.8'\n",
+    "except:\n",
+    "    !pip install neuraxle==0.5.8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Useful Constants\n",
+    "DATA_PATH = \"data/\"\n",
+    "DATASET_PATH = DATA_PATH + \"UCI HAR Dataset/\"\n",
+    "\n",
+    "# Those are separate normalised input features for the neural network\n",
+    "INPUT_SIGNAL_TYPES = [\n",
+    "    \"body_acc_x_\",\n",
+    "    \"body_acc_y_\",\n",
+    "    \"body_acc_z_\",\n",
+    "    \"body_gyro_x_\",\n",
+    "    \"body_gyro_y_\",\n",
+    "    \"body_gyro_z_\",\n",
+    "    \"total_acc_x_\",\n",
+    "    \"total_acc_y_\",\n",
+    "    \"total_acc_z_\"\n",
+    "]\n",
+    "\n",
+    "# Output classes to learn how to classify\n",
+    "LABELS = [\n",
+    "    \"WALKING\",\n",
+    "    \"WALKING_UPSTAIRS\",\n",
+    "    \"WALKING_DOWNSTAIRS\",\n",
+    "    \"SITTING\",\n",
+    "    \"STANDING\",\n",
+    "    \"LAYING\"\n",
+    "]\n",
+    "TRAIN = \"train/\"\n",
+    "TEST = \"test/\"\n",
+    "\n",
+    "\n",
+    "def load_all_data():\n",
+    "    # Load \"X\" (the neural network's training and testing inputs)\n",
+    "\n",
+    "    def load_X(X_signals_paths):\n",
+    "        X_signals = []\n",
+    "\n",
+    "        for signal_type_path in X_signals_paths:\n",
+    "            file = open(signal_type_path, 'r')\n",
+    "            # Read dataset from disk, dealing with text files' syntax\n",
+    "            X_signals.append(\n",
+    "                [np.array(serie, dtype=np.float32) for serie in [\n",
+    "                    row.replace('  ', ' ').strip().split(' ') for row in file\n",
+    "                ]]\n",
+    "            )\n",
+    "            file.close()\n",
+    "\n",
+    "        return np.transpose(np.array(X_signals), (1, 2, 0))\n",
+    "\n",
+    "    X_train_signals_paths = [\n",
+    "        DATASET_PATH + TRAIN + \"Inertial Signals/\" + signal + \"train.txt\" for signal in INPUT_SIGNAL_TYPES\n",
+    "    ]\n",
+    "    X_test_signals_paths = [\n",
+    "        DATASET_PATH + TEST + \"Inertial Signals/\" + signal + \"test.txt\" for signal in INPUT_SIGNAL_TYPES\n",
+    "    ]\n",
+    "\n",
+    "    X_train = load_X(X_train_signals_paths)\n",
+    "    X_test = load_X(X_test_signals_paths)\n",
+    "\n",
+    "    def load_y(y_path):\n",
+    "        file = open(y_path, 'r')\n",
+    "        # Read dataset from disk, dealing with text file's syntax\n",
+    "        y_ = np.array(\n",
+    "            [elem for elem in [\n",
+    "                row.replace('  ', ' ').strip().split(' ') for row in file\n",
+    "            ]],\n",
+    "            dtype=np.int32\n",
+    "        )\n",
+    "        file.close()\n",
+    "\n",
+    "        # Substract 1 to each output class for friendly 0-based indexing\n",
+    "        return y_ - 1\n",
+    "\n",
+    "    y_train_path = DATASET_PATH + TRAIN + \"y_train.txt\"\n",
+    "    y_test_path = DATASET_PATH + TEST + \"y_test.txt\"\n",
+    "\n",
+    "    y_train = load_y(y_train_path)\n",
+    "    y_test = load_y(y_test_path)\n",
+    "\n",
+    "    print(\"Some useful info to get an insight on dataset's shape and normalisation:\")\n",
+    "    print(\"(X shape, y shape, every X's mean, every X's standard deviation)\")\n",
+    "    print(X_test.shape, y_test.shape, np.mean(X_test), np.std(X_test))\n",
+    "\n",
+    "    return X_train, y_train, X_test, y_test\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Some useful info to get an insight on dataset's shape and normalisation:\n",
+      "(X shape, y shape, every X's mean, every X's standard deviation)\n",
+      "(2947, 128, 9) (2947, 1) 0.09913992 0.39567086\n",
+      "Dataset loaded!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Finally load dataset!\n",
+    "X_train, y_train, X_test, y_test = load_all_data()\n",
+    "print(\"Dataset loaded!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Part 1 - How would you code this in a typical ML project using Scikit-learn\n",
+    "\n",
+    "We'll first define functions that will extract features from the 3d data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "\n",
+    "def get_fft_peak_infos(real_fft, time_bins_axis=-2):\n",
+    "    \"\"\"\n",
+    "    Extract the indices of the bins with maximal amplitude, and the corresponding amplitude values.\n",
+    "\n",
+    "    :param fft: real magnitudes of an fft. It could be of shape [N, bins, features].\n",
+    "    :param time_bins_axis: axis of the frequency bins (e.g.: time axis before fft).\n",
+    "    :return: Two arrays without bins. One is an int, the other is a float. Shape: ([N, features], [N, features])\n",
+    "    \"\"\"\n",
+    "    peak_bin = np.argmax(real_fft, axis=time_bins_axis)\n",
+    "    peak_bin_val = np.max(real_fft, axis=time_bins_axis)\n",
+    "    return peak_bin, peak_bin_val\n",
+    "\n",
+    "\n",
+    "def fft_magnitudes(data_inputs, time_axis=-2):\n",
+    "    \"\"\"\n",
+    "    Apply a Fast Fourier Transform operation to analyze frequencies, and return real magnitudes.\n",
+    "    The bins past the half (past the nyquist frequency) are discarded, which result in shorter time series.\n",
+    "\n",
+    "    :param data_inputs: ND array of dimension at least 1. For instance, this could be of shape [N, time_axis, features]\n",
+    "    :param time_axis: axis along which the time series evolve\n",
+    "    :return: real magnitudes of the data_inputs. For instance, this could be of shape [N, (time_axis / 2) + 1, features]\n",
+    "             so here, we have `bins = (time_axis / 2) + 1`.\n",
+    "    \"\"\"\n",
+    "    fft = np.fft.rfft(data_inputs, axis=time_axis)\n",
+    "    real_fft = np.absolute(fft)\n",
+    "    return real_fft\n",
+    "\n",
+    "\n",
+    "def get_fft_features(x_data):\n",
+    "    \"\"\"\n",
+    "    Will featurize data with an FFT.\n",
+    "\n",
+    "    :param x_data: 3D time series of shape [batch_size, time_steps, sensors]\n",
+    "    :return: featurized time series with FFT of shape [batch_size, features]\n",
+    "    \"\"\"\n",
+    "    real_fft = fft_magnitudes(x_data)\n",
+    "    flattened_fft = real_fft.reshape(real_fft.shape[0], -1)\n",
+    "    peak_bin, peak_bin_val = get_fft_peak_infos(real_fft)\n",
+    "    return flattened_fft, peak_bin, peak_bin_val\n",
+    "\n",
+    "\n",
+    "def featurize_data(x_data):\n",
+    "    \"\"\"\n",
+    "    Will convert 3D time series of shape [batch_size, time_steps, sensors] to shape [batch_size, features]\n",
+    "    to prepare data for machine learning.\n",
+    "\n",
+    "    :param x_data: 3D time series of shape [batch_size, time_steps, sensors]\n",
+    "    :return: featurized time series of shape [batch_size, features]\n",
+    "    \"\"\"\n",
+    "    print(\"Input shape before feature union:\", x_data.shape)\n",
+    "\n",
+    "    flattened_fft, peak_bin, peak_bin_val = get_fft_features(x_data)\n",
+    "    mean = np.mean(x_data, axis=-2)\n",
+    "    median = np.median(x_data, axis=-2)\n",
+    "    min = np.min(x_data, axis=-2)\n",
+    "    max = np.max(x_data, axis=-2)\n",
+    "\n",
+    "    featurized_data = np.concatenate([\n",
+    "        flattened_fft,\n",
+    "        peak_bin,\n",
+    "        peak_bin_val,\n",
+    "        mean,\n",
+    "        median,\n",
+    "        min,\n",
+    "        max,\n",
+    "    ], axis=-1)\n",
+    "\n",
+    "    print(\"Shape after feature union, before classification:\", featurized_data.shape)\n",
+    "    return featurized_data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input shape before feature union: (7352, 128, 9)\n",
+      "Shape after feature union, before classification: (7352, 639)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeClassifier()"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Shape: [batch_size, time_steps, sensor_features]\n",
+    "X_train_featurized = featurize_data(X_train)\n",
+    "# Shape: [batch_size, remade_features]\n",
+    "\n",
+    "classifier = DecisionTreeClassifier()\n",
+    "classifier.fit(X_train_featurized, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input shape before feature union: (2947, 128, 9)\n",
+      "Shape after feature union, before classification: (2947, 639)\n",
+      "Shape at output after classification: (2947,)\n",
+      "Accuracy of sklearn pipeline code: 0.8642687478791992\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Shape: [batch_size, time_steps, sensor_features]\n",
+    "X_test_featurized = featurize_data(X_test)\n",
+    "# Shape: [batch_size, remade_features]\n",
+    "\n",
+    "y_pred = classifier.predict(X_test_featurized)\n",
+    "print(\"Shape at output after classification:\", y_pred.shape)\n",
+    "# Shape: [batch_size]\n",
+    "accuracy = accuracy_score(y_pred=y_pred, y_true=y_test)\n",
+    "print(\"Accuracy of sklearn pipeline code:\", accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 2 - How to code a similar pipeline - but cleaner - using Neuraxle\n",
+    "\n",
+    "To make ourselves a cleaner pipeline, we'll define each of our transformation as steps. Defining our pipeline in term of steps allows us to implement separately the behaviour on *fit* calls and on *transform* calls. In our present case though, we only need to define a *transform* behaviour."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neuraxle.base import BaseStep, NonFittableMixin\n",
+    "from neuraxle.steps.numpy import NumpyConcatenateInnerFeatures, NumpyShapePrinter, NumpyFlattenDatum, NumpyRavel\n",
+    "\n",
+    "class NumpyStep(NonFittableMixin, BaseStep):\n",
+    "    def __init__(self):\n",
+    "        BaseStep.__init__(self)\n",
+    "        NonFittableMixin.__init__(self)\n",
+    "\n",
+    "class NumpyFFT(NumpyStep):\n",
+    "    def transform(self, data_inputs):\n",
+    "        \"\"\"\n",
+    "        Featurize time series data with FFT.\n",
+    "\n",
+    "        :param data_inputs: time series data of 3D shape: [batch_size, time_steps, sensors_readings]\n",
+    "        :return: featurized data is of 2D shape: [batch_size, n_features]\n",
+    "        \"\"\"\n",
+    "        transformed_data = np.fft.rfft(data_inputs, axis=-2)\n",
+    "        return transformed_data\n",
+    "\n",
+    "\n",
+    "class FFTPeakBinWithValue(NumpyStep):\n",
+    "    def transform(self, data_inputs):\n",
+    "        \"\"\"\n",
+    "        Will compute peak fft bins (int), and their magnitudes' value (float), to concatenate them.\n",
+    "\n",
+    "        :param data_inputs: real magnitudes of an fft. It could be of shape [batch_size, bins, features].\n",
+    "        :return: Two arrays without bins concatenated on feature axis. Shape: [batch_size, 2 * features]\n",
+    "        \"\"\"\n",
+    "        time_bins_axis = -2\n",
+    "        peak_bin = np.argmax(data_inputs, axis=time_bins_axis)\n",
+    "        peak_bin_val = np.max(data_inputs, axis=time_bins_axis)\n",
+    "        \n",
+    "        # Notice that here another FeatureUnion could have been used with a joiner:\n",
+    "        transformed = np.concatenate([peak_bin, peak_bin_val], axis=-1)\n",
+    "        \n",
+    "        return transformed\n",
+    "\n",
+    "\n",
+    "class NumpyAbs(NumpyStep):\n",
+    "    def transform(self, data_inputs):\n",
+    "        \"\"\"\n",
+    "        Will featurize data with a max.\n",
+    "\n",
+    "        :param data_inputs: 3D time series of shape [batch_size, time_steps, sensors]\n",
+    "        :return: featurized time series of shape [batch_size, features]\n",
+    "        \"\"\"\n",
+    "        return np.abs(data_inputs)\n",
+    "\n",
+    "\n",
+    "class NumpyMean(NumpyStep):\n",
+    "    def transform(self, data_inputs):\n",
+    "        \"\"\"\n",
+    "        Will featurize data with a mean.\n",
+    "\n",
+    "        :param data_inputs: 3D time series of shape [batch_size, time_steps, sensors]\n",
+    "        :return: featurized time series of shape [batch_size, features]\n",
+    "        \"\"\"\n",
+    "        return np.mean(data_inputs, axis=-2)\n",
+    "\n",
+    "\n",
+    "class NumpyMedian(NumpyStep):\n",
+    "    def transform(self, data_inputs):\n",
+    "        \"\"\"\n",
+    "        Will featurize data with a median.\n",
+    "\n",
+    "        :param data_inputs: 3D time series of shape [batch_size, time_steps, sensors]\n",
+    "        :return: featurized time series of shape [batch_size, features]\n",
+    "        \"\"\"\n",
+    "        return np.median(data_inputs, axis=-2)\n",
+    "\n",
+    "\n",
+    "class NumpyMin(NumpyStep):\n",
+    "    def transform(self, data_inputs):\n",
+    "        \"\"\"\n",
+    "        Will featurize data with a min.\n",
+    "\n",
+    "        :param data_inputs: 3D time series of shape [batch_size, time_steps, sensors]\n",
+    "        :return: featurized time series of shape [batch_size, features]\n",
+    "        \"\"\"\n",
+    "        return np.min(data_inputs, axis=-2)\n",
+    "\n",
+    "\n",
+    "class NumpyMax(NumpyStep):\n",
+    "    def transform(self, data_inputs):\n",
+    "        \"\"\"\n",
+    "        Will featurize data with a max.\n",
+    "\n",
+    "        :param data_inputs: 3D time series of shape [batch_size, time_steps, sensors]\n",
+    "        :return: featurized time series of shape [batch_size, features]\n",
+    "        \"\"\"\n",
+    "        return np.max(data_inputs, axis=-2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we'll define a set of classifier we'd like to test and their respective hyperparameter space we'd like to explore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neuraxle.hyperparams.distributions import Choice, Boolean\n",
+    "from neuraxle.hyperparams.distributions import RandInt, LogUniform\n",
+    "from neuraxle.hyperparams.space import HyperparameterSpace\n",
+    "from neuraxle.pipeline import Pipeline\n",
+    "from neuraxle.steps.output_handlers import OutputTransformerWrapper\n",
+    "from neuraxle.steps.sklearn import SKLearnWrapper\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.linear_model import RidgeClassifier, LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier\n",
+    "\n",
+    "\n",
+    "decision_tree_classifier = SKLearnWrapper(\n",
+    "    DecisionTreeClassifier(), \n",
+    "    HyperparameterSpace({\n",
+    "        'criterion': Choice(['gini', 'entropy']), \n",
+    "        'splitter': Choice(['best', 'random']),\n",
+    "        'min_samples_leaf': RandInt(2, 5), \n",
+    "        'min_samples_split': Choice([0.5, 1.0, 2, 3]), \n",
+    "})).set_name('DecisionTreeClassifier')\n",
+    "\n",
+    "extra_tree_classifier = SKLearnWrapper(\n",
+    "    ExtraTreeClassifier(), \n",
+    "    HyperparameterSpace({\n",
+    "        'criterion': Choice(['gini', 'entropy']), \n",
+    "        'splitter': Choice(['best', 'random']),\n",
+    "        'min_samples_leaf': RandInt(2, 5), \n",
+    "        'min_samples_split': Choice([0.5, 1.0, 2, 3]), \n",
+    "})).set_name('ExtraTreeClassifier')\n",
+    "\n",
+    "ridge_classifier = Pipeline([OutputTransformerWrapper(NumpyRavel()), SKLearnWrapper(\n",
+    "    RidgeClassifier(), \n",
+    "    HyperparameterSpace({\n",
+    "        'alpha': Choice([0.0, 1.0, 10.0]), \n",
+    "        'fit_intercept': Boolean(), \n",
+    "        'normalize': Boolean()\n",
+    "    }))\n",
+    "]).set_name('RidgeClassifier')\n",
+    "\n",
+    "logistic_regression = Pipeline([OutputTransformerWrapper(NumpyRavel()), SKLearnWrapper(\n",
+    "    LogisticRegression(), \n",
+    "    HyperparameterSpace({\n",
+    "        'C': LogUniform(0.01, 10.0), \n",
+    "        'fit_intercept': Boolean(),\n",
+    "        'penalty': Choice(['none', 'l2']), \n",
+    "        'max_iter': RandInt(20, 200)\n",
+    "    }))\n",
+    "]).set_name('LogisticRegression')\n",
+    "\n",
+    "random_forest_classifier = Pipeline([OutputTransformerWrapper(NumpyRavel()), SKLearnWrapper(\n",
+    "    RandomForestClassifier(), \n",
+    "    HyperparameterSpace({\n",
+    "        'n_estimators': RandInt(50, 600), \n",
+    "        'criterion': Choice(['gini', 'entropy']),\n",
+    "        'min_samples_leaf': RandInt(2, 5), \n",
+    "        'min_samples_split': Choice([0.5, 1.0, 2, 3]),\n",
+    "        'bootstrap': Boolean()\n",
+    "    }))\n",
+    "]).set_name('RandomForestClassifier')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now have all the pieces to define a proper Neuraxle Pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neuraxle.steps.flow import TrainOnlyWrapper, ChooseOneStepOf\n",
+    "from neuraxle.steps.numpy import NumpyConcatenateInnerFeatures, NumpyShapePrinter, NumpyFlattenDatum\n",
+    "from neuraxle.union import FeatureUnion\n",
+    "\n",
+    "\n",
+    "pipeline = Pipeline([\n",
+    "    TrainOnlyWrapper(NumpyShapePrinter(custom_message=\"Input shape before feature union\")),\n",
+    "    FeatureUnion([\n",
+    "        Pipeline([\n",
+    "            NumpyFFT(),\n",
+    "            NumpyAbs(),\n",
+    "            FeatureUnion([\n",
+    "                NumpyFlattenDatum(),  # Reshape from 3D to flat 2D: flattening data except on batch size\n",
+    "                FFTPeakBinWithValue()  # Extract 2D features from the 3D FFT bins\n",
+    "            ], joiner=NumpyConcatenateInnerFeatures())\n",
+    "        ]),\n",
+    "        NumpyMean(),\n",
+    "        NumpyMedian(),\n",
+    "        NumpyMin(),\n",
+    "        NumpyMax()\n",
+    "    ], joiner=NumpyConcatenateInnerFeatures()),\n",
+    "    TrainOnlyWrapper(NumpyShapePrinter(custom_message=\"Shape after feature union, before classification\")),\n",
+    "    # Shape: [batch_size, remade_features]\n",
+    "    ChooseOneStepOf([\n",
+    "        decision_tree_classifier,\n",
+    "        extra_tree_classifier,\n",
+    "        ridge_classifier,\n",
+    "        logistic_regression,\n",
+    "        random_forest_classifier\n",
+    "    ]),\n",
+    "    TrainOnlyWrapper(NumpyShapePrinter(custom_message=\"Shape at output after classification\")),\n",
+    "    # Shape: [batch_size]\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have defined our pipeline, we can give it to an AutoML loop which will explore the hyperparameter space for us!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil \n",
+    "\n",
+    "# Clear cache if we've already ran the AutoML to start fresh:\n",
+    "cache_folder = 'cache'\n",
+    "if os.path.exists(cache_folder):\n",
+    "    shutil.rmtree(cache_folder)\n",
+    "os.makedirs(cache_folder, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from neuraxle.metaopt.auto_ml import AutoML, InMemoryHyperparamsRepository, ValidationSplitter, \\\n",
+    "    RandomSearchHyperparameterSelectionStrategy\n",
+    "#from neuraxle.metaopt.tpe import TreeParzenEstimatorSelectionStrategy\n",
+    "#from neuraxle.metaopt.auto_ml import HyperparamsJSONRepository\n",
+    "from neuraxle.metaopt.callbacks import ScoringCallback\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "\n",
+    "auto_ml = AutoML(\n",
+    "    pipeline=pipeline,\n",
+    "    hyperparams_optimizer=RandomSearchHyperparameterSelectionStrategy(),\n",
+    "    validation_splitter=ValidationSplitter(test_size=0.20),\n",
+    "    scoring_callback=ScoringCallback(accuracy_score, higher_score_is_better=True),\n",
+    "    n_trials=10,\n",
+    "    epochs=1,\n",
+    "    hyperparams_repository=InMemoryHyperparamsRepository(cache_folder=cache_folder),\n",
+    "    refit_trial=True,\n",
+    "    # callbacks=[MetricCallbacks(...)]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[12:14:24][INFO][auto_ml][246]: \n",
+      "new trial: {\n",
+      "    \"ChooseOneStepOf\": {\n",
+      "        \"DecisionTreeClassifier\": {\n",
+      "            \"Optional(DecisionTreeClassifier)\": {\n",
+      "                \"criterion\": \"entropy\",\n",
+      "                \"min_samples_leaf\": 3,\n",
+      "                \"min_samples_split\": 0.5,\n",
+      "                \"splitter\": \"random\"\n",
+      "            }\n",
+      "        },\n",
+      "        \"ExtraTreeClassifier\": {\n",
+      "            \"Optional(ExtraTreeClassifier)\": {\n",
+      "                \"criterion\": \"entropy\",\n",
+      "                \"min_samples_leaf\": 4,\n",
+      "                \"min_samples_split\": 2,\n",
+      "                \"splitter\": \"best\"\n",
+      "            }\n",
+      "        },\n",
+      "        \"LogisticRegression\": {\n",
+      "            \"Optional(LogisticRegression)\": {\n",
+      "                \"SKLearnWrapper_LogisticRegression\": {\n",
+      "                    \"C\": 0.0734038830915426,\n",
+      "                    \"fit_intercept\": false,\n",
+      "                    \"max_iter\": 52,\n",
+      "                    \"penalty\": \"none\"\n",
+      "                }\n",
+      "            }\n",
+      "        },\n",
+      "        \"RandomForestClassifier\": {\n",
+      "            \"Optional(RandomForestClassifier)\": {\n",
+      "                \"SKLearnWrapper_RandomForestClassifier\": {\n",
+      "                    \"bootstrap\": true,\n",
+      "                    \"criterion\": \"entropy\",\n",
+      "                    \"min_samples_leaf\": 3,\n",
+      "                    \"min_samples_split\": 2,\n",
+      "                    \"n_estimators\": 255\n",
+      "                }\n",
+      "            }\n",
+      "        },\n",
+      "        \"RidgeClassifier\": {\n",
+      "            \"Optional(RidgeClassifier)\": {\n",
+      "                \"SKLearnWrapper_RidgeClassifier\": {\n",
+      "                    \"alpha\": 0.0,\n",
+      "                    \"fit_intercept\": false,\n",
+      "                    \"normalize\": true\n",
+      "                }\n",
+      "            }\n",
+      "        },\n",
+      "        \"choice\": \"ExtraTreeClassifier\"\n",
+      "    }\n",
+      "}\n",
+      "[12:14:24][INFO][auto_ml][862]: trial 1/10\n",
+      "[12:14:24][INFO][auto_ml][590]: fitting trial 1/10 split 1/1\n",
+      "hyperparams: {\n",
+      "    \"ChooseOneStepOf\": {\n",
+      "        \"DecisionTreeClassifier\": {\n",
+      "            \"Optional(DecisionTreeClassifier)\": {\n",
+      "                \"ccp_alpha\": 0.0,\n",
+      "                \"class_weight\": null,\n",
+      "                \"criterion\": \"entropy\",\n",
+      "                \"max_depth\": null,\n",
+      "                \"max_features\": null,\n",
+      "                \"max_leaf_nodes\": null,\n",
+      "                \"min_impurity_decrease\": 0.0,\n",
+      "                \"min_impurity_split\": null,\n",
+      "                \"min_samples_leaf\": 3,\n",
+      "                \"min_samples_split\": 0.5,\n",
+      "                \"min_weight_fraction_leaf\": 0.0,\n",
+      "                \"random_state\": null,\n",
+      "                \"splitter\": \"random\"\n",
+      "            },\n",
+      "            \"enabled\": false\n",
+      "        },\n",
+      "        \"ExtraTreeClassifier\": {\n",
+      "            \"Optional(ExtraTreeClassifier)\": {\n",
+      "                \"ccp_alpha\": 0.0,\n",
+      "                \"class_weight\": null,\n",
+      "                \"criterion\": \"entropy\",\n",
+      "                \"max_depth\": null,\n",
+      "\n",
+      "\n",
+      "\n",
+      "[...]\n",
+      "[...]\n",
+      "[...]\n",
+      "\n",
+      "\n",
+      "\n",
+      "    },\n",
+      "    \"TrainOnlyWrapper2\": {\n",
+      "        \"NumpyShapePrinter\": {}\n",
+      "    }\n",
+      "}\n",
+      "[12:19:42][INFO][auto_ml][630]: epoch 1/1\n",
+      "NumpyShapePrinter: (5881, 128, 9) Input shape before feature union\n",
+      "NumpyShapePrinter: (5881, 639) Shape after feature union, before classification\n",
+      "[12:19:43][INFO][trial][450]: main train: 0.5279714334296888\n",
+      "[12:19:43][INFO][trial][471]: main validation: 0.5248130523453433\n",
+      "[12:19:43][INFO][auto_ml][605]: success trial 10/10 split 1/1\n",
+      "hyperparams: {\n",
+      "    \"ChooseOneStepOf\": {\n",
+      "        \"DecisionTreeClassifier\": {\n",
+      "            \"Optional(DecisionTreeClassifier)\": {\n",
+      "                \"ccp_alpha\": 0.0,\n",
+      "                \"class_weight\": null,\n",
+      "                \"criterion\": \"entropy\",\n",
+      "                \"max_depth\": null,\n",
+      "                \"max_features\": null,\n",
+      "                \"max_leaf_nodes\": null,\n",
+      "                \"min_impurity_decrease\": 0.0,\n",
+      "                \"min_impurity_split\": null,\n",
+      "                \"min_samples_leaf\": 3,\n",
+      "                \"min_samples_split\": 3,\n",
+      "                \"min_weight_fraction_leaf\": 0.0,\n",
+      "                \"random_state\": null,\n",
+      "                \"splitter\": \"random\"\n",
+      "            },\n",
+      "            \"enabled\": false\n",
+      "        },\n",
+      "        \"ExtraTreeClassifier\": {\n",
+      "            \"Optional(ExtraTreeClassifier)\": {\n",
+      "                \"ccp_alpha\": 0.0,\n",
+      "                \"class_weight\": null,\n",
+      "                \"criterion\": \"gini\",\n",
+      "                \"max_depth\": null,\n",
+      "                \"max_features\": \"auto\",\n",
+      "                \"max_leaf_nodes\": null,\n",
+      "                \"min_impurity_decrease\": 0.0,\n",
+      "                \"min_impurity_split\": null,\n",
+      "                \"min_samples_leaf\": 5,\n",
+      "                \"min_samples_split\": 0.5,\n",
+      "                \"min_weight_fraction_leaf\": 0.0,\n",
+      "                \"random_state\": null,\n",
+      "                \"splitter\": \"best\"\n",
+      "            },\n",
+      "            \"enabled\": true\n",
+      "        },\n",
+      "        \"LogisticRegression\": {\n",
+      "            \"Optional(LogisticRegression)\": {\n",
+      "                \"OutputTransformerWrapper\": {\n",
+      "                    \"NumpyRavel\": {}\n",
+      "                },\n",
+      "                \"SKLearnWrapper_LogisticRegression\": {\n",
+      "                    \"C\": 8.175969462206426,\n",
+      "                    \"class_weight\": null,\n",
+      "                    \"dual\": false,\n",
+      "                    \"fit_intercept\": true,\n",
+      "                    \"intercept_scaling\": 1,\n",
+      "                    \"l1_ratio\": null,\n",
+      "                    \"max_iter\": 178,\n",
+      "                    \"multi_class\": \"auto\",\n",
+      "                    \"n_jobs\": null,\n",
+      "                    \"penalty\": \"l2\",\n",
+      "                    \"random_state\": null,\n",
+      "                    \"solver\": \"lbfgs\",\n",
+      "                    \"tol\": 0.0001,\n",
+      "                    \"verbose\": 0,\n",
+      "                    \"warm_start\": false\n",
+      "                }\n",
+      "            },\n",
+      "            \"enabled\": false\n",
+      "        },\n",
+      "        \"RandomForestClassifier\": {\n",
+      "            \"Optional(RandomForestClassifier)\": {\n",
+      "                \"OutputTransformerWrapper\": {\n",
+      "                    \"NumpyRavel\": {}\n",
+      "                },\n",
+      "                \"SKLearnWrapper_RandomForestClassifier\": {\n",
+      "                    \"bootstrap\": false,\n",
+      "                    \"ccp_alpha\": 0.0,\n",
+      "                    \"class_weight\": null,\n",
+      "                    \"criterion\": \"entropy\",\n",
+      "                    \"max_depth\": null,\n",
+      "                    \"max_features\": \"auto\",\n",
+      "                    \"max_leaf_nodes\": null,\n",
+      "                    \"max_samples\": null,\n",
+      "                    \"min_impurity_decrease\": 0.0,\n",
+      "                    \"min_impurity_split\": null,\n",
+      "                    \"min_samples_leaf\": 4,\n",
+      "                    \"min_samples_split\": 2,\n",
+      "                    \"min_weight_fraction_leaf\": 0.0,\n",
+      "                    \"n_estimators\": 133,\n",
+      "                    \"n_jobs\": null,\n",
+      "                    \"oob_score\": false,\n",
+      "                    \"random_state\": null,\n",
+      "                    \"verbose\": 0,\n",
+      "                    \"warm_start\": false\n",
+      "                }\n",
+      "            },\n",
+      "            \"enabled\": false\n",
+      "        },\n",
+      "        \"RidgeClassifier\": {\n",
+      "            \"Optional(RidgeClassifier)\": {\n",
+      "                \"OutputTransformerWrapper\": {\n",
+      "                    \"NumpyRavel\": {}\n",
+      "                },\n",
+      "                \"SKLearnWrapper_RidgeClassifier\": {\n",
+      "                    \"alpha\": 10.0,\n",
+      "                    \"class_weight\": null,\n",
+      "                    \"copy_X\": true,\n",
+      "                    \"fit_intercept\": false,\n",
+      "                    \"max_iter\": null,\n",
+      "                    \"normalize\": true,\n",
+      "                    \"random_state\": null,\n",
+      "                    \"solver\": \"auto\",\n",
+      "                    \"tol\": 0.001\n",
+      "                }\n",
+      "            },\n",
+      "            \"enabled\": false\n",
+      "        },\n",
+      "        \"choice\": \"ExtraTreeClassifier\",\n",
+      "        \"joiner\": {}\n",
+      "    },\n",
+      "    \"FeatureUnion\": {\n",
+      "        \"NumpyMax\": {},\n",
+      "        \"NumpyMean\": {},\n",
+      "        \"NumpyMedian\": {},\n",
+      "        \"NumpyMin\": {},\n",
+      "        \"Pipeline\": {\n",
+      "            \"FeatureUnion\": {\n",
+      "                \"FFTPeakBinWithValue\": {},\n",
+      "                \"NumpyFlattenDatum\": {},\n",
+      "                \"joiner\": {}\n",
+      "            },\n",
+      "            \"NumpyAbs\": {},\n",
+      "            \"NumpyFFT\": {}\n",
+      "        },\n",
+      "        \"joiner\": {}\n",
+      "    },\n",
+      "    \"TrainOnlyWrapper\": {\n",
+      "        \"NumpyShapePrinter\": {}\n",
+      "    },\n",
+      "    \"TrainOnlyWrapper1\": {\n",
+      "        \"NumpyShapePrinter\": {}\n",
+      "    },\n",
+      "    \"TrainOnlyWrapper2\": {\n",
+      "        \"NumpyShapePrinter\": {}\n",
+      "    }\n",
+      "}\n",
+      "best score: 0.5248130523453433 at epoch 0\n",
+      "[12:19:43][INFO][auto_ml][832]: \n",
+      "best hyperparams: {\n",
+      "    \"ChooseOneStepOf\": {\n",
+      "        \"DecisionTreeClassifier\": {\n",
+      "            \"Optional(DecisionTreeClassifier)\": {\n",
+      "                \"ccp_alpha\": 0.0,\n",
+      "                \"class_weight\": null,\n",
+      "                \"criterion\": \"gini\",\n",
+      "                \"max_depth\": null,\n",
+      "                \"max_features\": null,\n",
+      "                \"max_leaf_nodes\": null,\n",
+      "                \"min_impurity_decrease\": 0.0,\n",
+      "                \"min_impurity_split\": null,\n",
+      "                \"min_samples_leaf\": 2,\n",
+      "                \"min_samples_split\": 0.5,\n",
+      "                \"min_weight_fraction_leaf\": 0.0,\n",
+      "                \"random_state\": null,\n",
+      "                \"splitter\": \"random\"\n",
+      "            },\n",
+      "            \"enabled\": false\n",
+      "        },\n",
+      "        \"ExtraTreeClassifier\": {\n",
+      "            \"Optional(ExtraTreeClassifier)\": {\n",
+      "                \"ccp_alpha\": 0.0,\n",
+      "                \"class_weight\": null,\n",
+      "                \"criterion\": \"entropy\",\n",
+      "                \"max_depth\": null,\n",
+      "                \"max_features\": \"auto\",\n",
+      "                \"max_leaf_nodes\": null,\n",
+      "                \"min_impurity_decrease\": 0.0,\n",
+      "                \"min_impurity_split\": null,\n",
+      "                \"min_samples_leaf\": 4,\n",
+      "                \"min_samples_split\": 1.0,\n",
+      "                \"min_weight_fraction_leaf\": 0.0,\n",
+      "                \"random_state\": null,\n",
+      "                \"splitter\": \"best\"\n",
+      "            },\n",
+      "            \"enabled\": false\n",
+      "        },\n",
+      "        \"LogisticRegression\": {\n",
+      "            \"Optional(LogisticRegression)\": {\n",
+      "                \"OutputTransformerWrapper\": {\n",
+      "                    \"NumpyRavel\": {}\n",
+      "                },\n",
+      "                \"SKLearnWrapper_LogisticRegression\": {\n",
+      "                    \"C\": 6.020923240680797,\n",
+      "                    \"class_weight\": null,\n",
+      "                    \"dual\": false,\n",
+      "                    \"fit_intercept\": false,\n",
+      "                    \"intercept_scaling\": 1,\n",
+      "                    \"l1_ratio\": null,\n",
+      "                    \"max_iter\": 98,\n",
+      "                    \"multi_class\": \"auto\",\n",
+      "                    \"n_jobs\": null,\n",
+      "                    \"penalty\": \"none\",\n",
+      "                    \"random_state\": null,\n",
+      "                    \"solver\": \"lbfgs\",\n",
+      "                    \"tol\": 0.0001,\n",
+      "                    \"verbose\": 0,\n",
+      "                    \"warm_start\": false\n",
+      "                }\n",
+      "            },\n",
+      "            \"enabled\": true\n",
+      "        },\n",
+      "        \"RandomForestClassifier\": {\n",
+      "            \"Optional(RandomForestClassifier)\": {\n",
+      "                \"OutputTransformerWrapper\": {\n",
+      "                    \"NumpyRavel\": {}\n",
+      "                },\n",
+      "                \"SKLearnWrapper_RandomForestClassifier\": {\n",
+      "                    \"bootstrap\": true,\n",
+      "                    \"ccp_alpha\": 0.0,\n",
+      "                    \"class_weight\": null,\n",
+      "                    \"criterion\": \"entropy\",\n",
+      "                    \"max_depth\": null,\n",
+      "                    \"max_features\": \"auto\",\n",
+      "                    \"max_leaf_nodes\": null,\n",
+      "                    \"max_samples\": null,\n",
+      "                    \"min_impurity_decrease\": 0.0,\n",
+      "                    \"min_impurity_split\": null,\n",
+      "                    \"min_samples_leaf\": 2,\n",
+      "                    \"min_samples_split\": 3,\n",
+      "                    \"min_weight_fraction_leaf\": 0.0,\n",
+      "                    \"n_estimators\": 510,\n",
+      "                    \"n_jobs\": null,\n",
+      "                    \"oob_score\": false,\n",
+      "                    \"random_state\": null,\n",
+      "                    \"verbose\": 0,\n",
+      "                    \"warm_start\": false\n",
+      "                }\n",
+      "            },\n",
+      "            \"enabled\": false\n",
+      "        },\n",
+      "        \"RidgeClassifier\": {\n",
+      "            \"Optional(RidgeClassifier)\": {\n",
+      "                \"OutputTransformerWrapper\": {\n",
+      "                    \"NumpyRavel\": {}\n",
+      "                },\n",
+      "                \"SKLearnWrapper_RidgeClassifier\": {\n",
+      "                    \"alpha\": 1.0,\n",
+      "                    \"class_weight\": null,\n",
+      "                    \"copy_X\": true,\n",
+      "                    \"fit_intercept\": false,\n",
+      "                    \"max_iter\": null,\n",
+      "                    \"normalize\": true,\n",
+      "                    \"random_state\": null,\n",
+      "                    \"solver\": \"auto\",\n",
+      "                    \"tol\": 0.001\n",
+      "                }\n",
+      "            },\n",
+      "            \"enabled\": false\n",
+      "        },\n",
+      "        \"choice\": \"LogisticRegression\",\n",
+      "        \"joiner\": {}\n",
+      "    },\n",
+      "    \"FeatureUnion\": {\n",
+      "        \"NumpyMax\": {},\n",
+      "        \"NumpyMean\": {},\n",
+      "        \"NumpyMedian\": {},\n",
+      "        \"NumpyMin\": {},\n",
+      "        \"Pipeline\": {\n",
+      "            \"FeatureUnion\": {\n",
+      "                \"FFTPeakBinWithValue\": {},\n",
+      "                \"NumpyFlattenDatum\": {},\n",
+      "                \"joiner\": {}\n",
+      "            },\n",
+      "            \"NumpyAbs\": {},\n",
+      "            \"NumpyFFT\": {}\n",
+      "        },\n",
+      "        \"joiner\": {}\n",
+      "    },\n",
+      "    \"TrainOnlyWrapper\": {\n",
+      "        \"NumpyShapePrinter\": {}\n",
+      "    },\n",
+      "    \"TrainOnlyWrapper1\": {\n",
+      "        \"NumpyShapePrinter\": {}\n",
+      "    },\n",
+      "    \"TrainOnlyWrapper2\": {\n",
+      "        \"NumpyShapePrinter\": {}\n",
+      "    }\n",
+      "}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NumpyShapePrinter: (7352, 128, 9) Input shape before feature union\n",
+      "NumpyShapePrinter: (7352, 639) Shape after feature union, before classification\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:1323: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
+      "  \"Setting penalty='none' will ignore the C and l1_ratio \"\n",
+      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
+     ]
+    }
+   ],
+   "source": [
+    "auto_ml = auto_ml.fit(X_train, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy score: 0.9365456396335257\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_pipeline = auto_ml.get_best_model()\n",
+    "y_pred = best_pipeline.predict(X_test)\n",
+    "\n",
+    "accuracy = accuracy_score(y_true=y_test, y_pred=y_pred)\n",
+    "print(\"Test accuracy score:\", accuracy)\n",
+    "assert accuracy > 0.9, \"Try again harder!\"\n",
+    "# It's getting good on this dataset if you're over 92%. The current code is able to do this."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/auto_ml/plot_automl_loop_clean_kata.py b/examples/auto_ml/plot_automl_loop_clean_kata.py
index 4cb1c712..8385676f 100644
--- a/examples/auto_ml/plot_automl_loop_clean_kata.py
+++ b/examples/auto_ml/plot_automl_loop_clean_kata.py
@@ -49,48 +49,49 @@ def main():
     # See also HyperparameterSpace documentation : https://www.neuraxle.org/stable/api/neuraxle.hyperparams.space.html#neuraxle.hyperparams.space.HyperparameterSpace
 
     decision_tree_classifier = SKLearnWrapper(
-        DecisionTreeClassifier(), HyperparameterSpace({
+        DecisionTreeClassifier(),
+        HyperparameterSpace({
             'criterion': Choice(['gini', 'entropy']),
             'splitter': Choice(['best', 'random']),
             'min_samples_leaf': RandInt(2, 5),
-            'min_samples_split': RandInt(1, 3)
+            'min_samples_split': RandInt(2, 4)
         }))
 
     extra_tree_classifier = SKLearnWrapper(
-        ExtraTreeClassifier(), HyperparameterSpace({
+        ExtraTreeClassifier(),
+        HyperparameterSpace({
             'criterion': Choice(['gini', 'entropy']),
             'splitter': Choice(['best', 'random']),
             'min_samples_leaf': RandInt(2, 5),
-            'min_samples_split': RandInt(1, 3)
+            'min_samples_split': RandInt(2, 4)
         }))
 
-    ridge_classifier = Pipeline([
-        OutputTransformerWrapper(NumpyRavel()),
-        SKLearnWrapper(RidgeClassifier(), HyperparameterSpace({
-            'alpha': Choice([(0.0, 1.0, 10.0), (0.0, 10.0, 100.0)]),
+    ridge_classifier = Pipeline([OutputTransformerWrapper(NumpyRavel()), SKLearnWrapper(
+        RidgeClassifier(),
+        HyperparameterSpace({
+            'alpha': Choice([0.0, 1.0, 10.0, 100.0]),
             'fit_intercept': Boolean(),
             'normalize': Boolean()
         }))
     ]).set_name('RidgeClassifier')
 
-    logistic_regression = Pipeline([
-        OutputTransformerWrapper(NumpyRavel()),
-        SKLearnWrapper(LogisticRegression(), HyperparameterSpace({
+    logistic_regression = Pipeline([OutputTransformerWrapper(NumpyRavel()), SKLearnWrapper(
+        LogisticRegression(),
+        HyperparameterSpace({
             'C': LogUniform(0.01, 10.0),
             'fit_intercept': Boolean(),
-            'dual': Boolean(),
-            'penalty': Choice(['l1', 'l2']),
+            'penalty': Choice(['none', 'l2']),
             'max_iter': RandInt(20, 200)
         }))
     ]).set_name('LogisticRegression')
 
-    random_forest_classifier = Pipeline([
-        OutputTransformerWrapper(NumpyRavel()),
-        SKLearnWrapper(RandomForestClassifier(), HyperparameterSpace({
+    random_forest_classifier = Pipeline([OutputTransformerWrapper(NumpyRavel()), SKLearnWrapper(
+        RandomForestClassifier(),
+        HyperparameterSpace({
             'n_estimators': RandInt(50, 600),
             'criterion': Choice(['gini', 'entropy']),
             'min_samples_leaf': RandInt(2, 5),
-            'min_samples_split': RandInt(1, 3),
+            'min_samples_split': RandInt(2, 4),
             'bootstrap': Boolean()
         }))
     ]).set_name('RandomForestClassifier')
@@ -120,6 +121,7 @@ def main():
         epochs=1,
         hyperparams_repository=HyperparamsJSONRepository(cache_folder='cache'),
         refit_trial=True,
+        continue_loop_on_error=False
     )
 
     # Load data, and launch AutoML loop !
diff --git a/examples/auto_ml/time-series-data.jpg b/examples/auto_ml/time-series-data.jpg
new file mode 100644
index 00000000..d0cfc51f
Binary files /dev/null and b/examples/auto_ml/time-series-data.jpg differ
diff --git a/neuraxle/__init__.py b/neuraxle/__init__.py
index fc0a8435..906d362f 100644
--- a/neuraxle/__init__.py
+++ b/neuraxle/__init__.py
@@ -1,1 +1,1 @@
-__version__ = "0.5.8"
+__version__ = "0.6.0"
diff --git a/neuraxle/base.py b/neuraxle/base.py
index 09f864ff..1498584c 100644
--- a/neuraxle/base.py
+++ b/neuraxle/base.py
@@ -806,6 +806,9 @@ def handle_transform(self, data_container: DataContainer, context: ExecutionCont
         :param context: execution context
         :return: transformed data container
         """
+        if not self.is_initialized:
+            self.setup(context)
+
         data_container, context = self._will_process(data_container, context)
         data_container, context = self._will_transform_data_container(data_container, context)
 
@@ -887,6 +890,9 @@ def handle_fit(self, data_container: DataContainer, context: ExecutionContext) -
             :class:`~neuraxle.data_container.DataContainer`,
             :class:`~neuraxle.pipeline.Pipeline`
         """
+        if not self.is_initialized:
+            self.setup(context)
+
         self._did_process(data_container, context)
         return self
 
@@ -967,6 +973,9 @@ def handle_inverse_transform(self, data_container: DataContainer, context: Execu
             :class:`~neuraxle.data_container.DataContainer`,
             :class:`~neuraxle.pipeline.Pipeline`
         """
+        if not self.is_initialized:
+            self.setup(context)
+
         data_container, context = self._will_process(data_container, context)
         data_container = self._inverse_transform_data_container(data_container, context)
         data_container = self._did_process(data_container, context)
@@ -1001,6 +1010,13 @@ def inverse_transform(self, processed_outputs):
         """
         raise NotImplementedError("TODO: Implement this method in {}.".format(self.__class__.__name__))
 
+    def teardown(self)->'BaseTransformer':
+        """
+        Applies _teardown on the step and, if applicable, its children.
+        :return: self
+        """
+        self.apply("_teardown")
+        return self
 
 class _FittableStep:
     """
@@ -1022,6 +1038,9 @@ def handle_fit(self, data_container: DataContainer, context: ExecutionContext) -
         :param context: execution context
         :return: tuple(fitted pipeline, data_container)
         """
+        if not self.is_initialized:
+            self.setup(context)
+
         data_container, context = self._will_process(data_container, context)
         data_container, context = self._will_fit(data_container, context)
 
@@ -1106,6 +1125,9 @@ def handle_fit_transform(self, data_container: DataContainer, context: Execution
         :param context: execution context
         :return: tuple(fitted pipeline, data_container)
         """
+        if not self.is_initialized:
+            self.setup(context)
+
         data_container, context = self._will_process(data_container, context)
         data_container, context = self._will_fit_transform(data_container, context)
 
@@ -1200,6 +1222,8 @@ def handle_fit(self, data_container: DataContainer, context: ExecutionContext) -
             :class:`~neuraxle.data_container.DataContainer`,
             :class:`~neuraxle.base.ExecutionContext`
         """
+        if not self.is_initialized:
+            self.setup(context)
         data_container, context = self._will_process(data_container, context)
         data_container, context = self._will_fit(data_container, context)
 
@@ -1226,6 +1250,9 @@ def handle_fit_transform(self, data_container: DataContainer, context: Execution
             :class:`~neuraxle.data_container.DataContainer`,
             :class:`~neuraxle.base.ExecutionContext`
         """
+        if not self.is_initialized:
+            self.setup(context)
+
         data_container, context = self._will_process(data_container, context)
         data_container, context = self._will_fit_transform(data_container, context)
 
@@ -1251,6 +1278,9 @@ def handle_transform(self, data_container: DataContainer, context: ExecutionCont
             :class:`~neuraxle.data_container.DataContainer`,
             :class:`~neuraxle.base.ExecutionContext`
         """
+        if not self.is_initialized:
+            self.setup(context)
+
         data_container, context = self._will_process(data_container, context)
         data_container, context = self._will_transform_data_container(data_container, context)
 
@@ -2148,7 +2178,10 @@ def setup(self, context: ExecutionContext = None) -> 'BaseTransformer':
         Initialize the step before it runs. Only from here and not before that heavy things should be created
         (e.g.: things inside GPU), and NOT in the constructor.
 
-        The setup method is called for each step before any fit, or fit_transform.
+        .. warning::
+            The setup method is called once for each step when handle_fit, handle_fit_transform or handle_transform is called.
+            The setup method is executed only if is self.is_initialized is False
+            A setup function should set the self.is_initialized to True when called.
 
         :param context: execution context
         :return: self
@@ -2184,7 +2217,7 @@ def _invalidate(self):
         self.is_invalidated = True
         return RecursiveDict()
 
-    def teardown(self) -> 'BaseTransformer':
+    def _teardown(self) -> 'BaseTransformer':
         """
         Teardown step after program execution. Inverse of setup, and it should clear memory.
         Override this method if you need to clear memory.
@@ -2194,6 +2227,13 @@ def teardown(self) -> 'BaseTransformer':
         self.is_initialized = False
         return self
 
+    def __del__(self):
+        try:
+            self._teardown()
+        except Exception:
+            import traceback
+            print(traceback.format_exc())
+
     def set_train(self, is_train: bool = True):
         """
         This method overrides the method of BaseStep to also consider the wrapped step as well as self.
@@ -2403,19 +2443,6 @@ def _apply_childrens(self, results: RecursiveDict, method: Union[str, Callable],
 
         return results
 
-    def setup(self, context: ExecutionContext = None) -> BaseTransformer:
-        """
-        Initialize step before it runs. Also initialize its childrens.
-
-        :param context: execution context
-        :return: self
-        """
-        super().setup(context=context)
-        for step in self.get_children():
-            step.setup(context=context)
-        self.is_initialized = True
-        return self
-
     @abstractmethod
     def get_children(self) -> List[BaseStep]:
         """
@@ -2471,7 +2498,6 @@ def fit_transform(self, data_inputs, expected_outputs=None):
 
     .. seealso::
         :class:`~neuraxle.steps.loop.ForEachDataInput`,
-        :class:`~neuraxle.metaopt.sklearn.MetaSKLearnWrapper`,
         :class:`~neuraxle.metaopt.random.BaseCrossValidationWrapper`,
         :class:`~neuraxle.steps.caching.ValueCachingWrapper`,
         :class:`~neuraxle.steps.loop.StepClonerForEachDataInput`
@@ -2496,17 +2522,6 @@ def set_step(self, step: BaseTransformer) -> BaseStep:
         self.wrapped: BaseTransformer = _sklearn_to_neuraxle_step(step)
         return self
 
-    def teardown(self) -> BaseStep:
-        """
-        Teardown step. Also teardown the wrapped step.
-
-        :return: self
-        """
-        super().teardown()
-        self.wrapped.teardown()
-        self.is_initialized = False
-        return self
-
     def get_step(self) -> BaseStep:
         """
         Get wrapped step
@@ -2935,20 +2950,6 @@ def set_steps(self, steps_as_tuple: NamedTupleList):
         self.steps_as_tuple: NamedTupleList = self._patch_missing_names(steps_as_tuple)
         self._refresh_steps()
 
-    def teardown(self) -> 'BaseTransformer':
-        """
-        Teardown step after program execution.
-        Teardowns all of the sub steps as well.
-
-        :return: self
-        """
-        for step_name, step in self.steps_as_tuple:
-            step.teardown()
-
-        self.is_initialized = False
-
-        return self
-
     def get_children(self) -> List[BaseStep]:
         """
         Get the list of sub step inside the step with children.
diff --git a/neuraxle/data_container.py b/neuraxle/data_container.py
index 42e8b50c..1cea34a1 100644
--- a/neuraxle/data_container.py
+++ b/neuraxle/data_container.py
@@ -26,6 +26,7 @@
 import copy
 import hashlib
 import math
+from operator import attrgetter
 from typing import Any, Iterable, List, Tuple, Union
 
 import numpy as np
@@ -433,46 +434,43 @@ class ZipDataContainer(DataContainer):
     """
 
     @staticmethod
-    def create_from(data_container: DataContainer, *other_data_containers: DataContainer) -> 'ZipDataContainer':
+    def create_from(data_container: DataContainer, *other_data_containers: List[DataContainer], zip_expected_outputs:bool=False) -> 'ZipDataContainer':
         """
         Merges two data sources together. Zips only the data input part and keeps the expected output of the first DataContainer as is.
+        NOTE: Expects that all DataContainer are at least as long as data_container.
 
-        :param data_container: data container to transform
+        :param data_container: the main data container, the attribute of this data container will be kept by the returned ZipDataContainer.
         :type data_container: DataContainer
         :param other_data_containers: other data containers to zip with data container
         :type other_data_containers: List[DataContainer]
+        :param zip_expected_outputs: Determines wether we kept the expected_output of data_container or we zip the expected_outputs of all DataContainer provided
         :return: expanded data container
         :rtype: ExpandedDataContainer
         """
-        new_data_inputs = []
-
-        for i, (_, di, eo) in enumerate(data_container):
-            new_data_input = [di]
 
-            for other_data_container in other_data_containers:
-                _, di, eo = other_data_container[i]
-                new_data_input.append(di)
-
-            new_data_inputs.append(tuple(new_data_input))
+        new_data_inputs = tuple(zip(*map(attrgetter("data_inputs"), [data_container] + list(other_data_containers))))
+        if zip_expected_outputs:
+            expected_outputs = tuple(zip(*map(attrgetter("expected_outputs"), [data_container] + list(other_data_containers))))
+        else:
+            expected_outputs = data_container.expected_outputs
 
         return ZipDataContainer(
             data_inputs=new_data_inputs,
+            expected_outputs=expected_outputs,
             current_ids=data_container.current_ids,
             summary_id=data_container.summary_id,
-            expected_outputs=data_container.expected_outputs,
             sub_data_containers=data_container.sub_data_containers
         )
 
     def concatenate_inner_features(self):
         """
         Concatenate inner features from zipped data inputs.
-        Broadcast data inputs if the dimension is smaller.
+        Assumes each data_input entry is an iterable of numpy arrays.
         """
         new_data_inputs = [di[0] for di in self.data_inputs]
 
         for i, data_input in enumerate(self.data_inputs):
-            for di in data_input[1:]:
-                new_data_inputs[i] = _inner_concatenate_np_array(new_data_inputs[i], di)
+            new_data_inputs[i] = _inner_concatenate_np_array(list(data_input))
 
         self.set_data_inputs(new_data_inputs)
 
@@ -611,21 +609,30 @@ def _pad_data(data: Iterable, default_value: Any, batch_size: int):
     return data_
 
 
-def _inner_concatenate_np_array(np_array, np_array_to_zip):
+def _inner_concatenate_np_array(np_arrays_to_concatenate: List[np.ndarray]):
     """
-    Concatenate numpy arrays on the last axis using expand dim, and broadcasting.
+    Concatenate numpy arrays on the last axis, expanding and broadcasting if necessary.
 
-    :param np_array: data container
-    :type np_array: np.ndarray
-    :param np_array_to_zip: numpy array to zip with the other
-    :type np_array_to_zip: np.ndarray
+    :param np_arrays_to_concatenate: numpy arrays to zip with the other
+    :type np_arrays_to_concatenate: Iterable[np.ndarray]
     :return: concatenated np array
     :rtype: np.ndarray
     """
-    while len(np_array_to_zip.shape) < len(np_array.shape):
-        np_array_to_zip = np.expand_dims(np_array_to_zip, axis=-1)
+    n_arrays = len(np_arrays_to_concatenate)
+    target_n_dims = max(map(lambda x: len(x.shape), np_arrays_to_concatenate))
+    # These three next lines may be a bit hard to follow;
+    # they compute the max for every dimensions of the np arrays except the last.
+    # Previous code just assumed np_arrays_to_concatenate[0].shape[:-1]
+    _temp = map(attrgetter('shape'), np_arrays_to_concatenate)
+    _temp = map(lambda x: list(x)+[0 for _ in range(target_n_dims-len(x))], _temp)
+    target_dims_m1 = list(map(max, *list(_temp)))[:-1]
+
+    for i in range(n_arrays):
+        while len(np_arrays_to_concatenate[i].shape) < target_n_dims:
+            np_arrays_to_concatenate[i] = np.expand_dims(np_arrays_to_concatenate[i], axis=-1)
+
+        target_shape = tuple(target_dims_m1 + [np_arrays_to_concatenate[i].shape[-1]])
+        np_arrays_to_concatenate[i] = np.broadcast_to(np_arrays_to_concatenate[i], target_shape)
 
-    target_shape = tuple(list(np_array.shape[:-1]) + [np_array_to_zip.shape[-1]])
-    np_array_to_zip = np.broadcast_to(np_array_to_zip, target_shape)
+    return np.concatenate(np_arrays_to_concatenate, axis=-1)
 
-    return np.concatenate((np_array, np_array_to_zip), axis=-1)
diff --git a/neuraxle/distributed/streaming.py b/neuraxle/distributed/streaming.py
index 97d463d5..cad2ec3a 100644
--- a/neuraxle/distributed/streaming.py
+++ b/neuraxle/distributed/streaming.py
@@ -54,7 +54,7 @@ def __init__(self, queue):
         self.observers = []
         self._add_observable_queue_step_saver()
 
-    def teardown(self):
+    def _teardown(self):
         self.queue = None
         return self
 
@@ -197,7 +197,7 @@ def start(self, context: ExecutionContext):
             p.start()
             self.workers.append(p)
 
-    def teardown(self):
+    def _teardown(self):
         """
         Stop all processes on teardown.
 
@@ -688,7 +688,7 @@ def __init__(self, batch_size, n_batches=None):
         Joiner.__init__(self, batch_size=batch_size)
         ObservableQueueMixin.__init__(self, Queue())
 
-    def teardown(self) -> 'BaseTransformer':
+    def _teardown(self) -> 'BaseTransformer':
         """
         Properly clean queue, summary ids, and results during teardown.
 
diff --git a/neuraxle/metaopt/auto_ml.py b/neuraxle/metaopt/auto_ml.py
index 405146cb..5eefafa8 100644
--- a/neuraxle/metaopt/auto_ml.py
+++ b/neuraxle/metaopt/auto_ml.py
@@ -25,6 +25,7 @@
 """
 
 import copy
+import gc
 import glob
 import hashlib
 import json
@@ -124,6 +125,12 @@ def get_best_hyperparams(self) -> HyperparameterSamples:
         best_hyperparams = HyperparameterSamples(trials.get_best_hyperparams())
         return best_hyperparams
 
+    def _save_best_model(self, step: BaseStep, trial_hash: str):
+        step.set_name(trial_hash).save(ExecutionContext(self.best_retrained_model_folder), full_dump=True)
+
+    def _load_best_model(self, trial_hash: str) -> BaseStep:
+        return ExecutionContext(str(self.best_retrained_model_folder)).load(trial_hash)
+
     def get_best_model(self):
         """
         Load the best model saved inside the best retrained model folder.
@@ -132,8 +139,7 @@ def get_best_model(self):
         """
         hyperparams: HyperparameterSamples = self.get_best_hyperparams()
         trial_hash: str = self._get_trial_hash(HyperparameterSamples(hyperparams).to_flat_dict())
-        p: BaseStep = ExecutionContext(str(self.best_retrained_model_folder)).load(trial_hash)
-
+        p: BaseStep = self._load_best_model(trial_hash)
         return p
 
     def save_best_model(self, step: BaseStep):
@@ -145,8 +151,7 @@ def save_best_model(self, step: BaseStep):
         """
         hyperparams = step.get_hyperparams().to_flat_dict()
         trial_hash = self._get_trial_hash(hyperparams)
-        step.set_name(trial_hash).save(ExecutionContext(self.best_retrained_model_folder), full_dump=True)
-
+        self._save_best_model(step, trial_hash)
         return step
 
     @abstractmethod
@@ -498,12 +503,10 @@ def __init__(
         callbacks: List[BaseCallback] = [scoring_callback] + callbacks
         self.callbacks: CallbackList = CallbackList(callbacks)
 
-
         if hyperparams_repository is None:
             hyperparams_repository = InMemoryHyperparamsRepository()
         self.hyperparams_repository: HyperparamsRepository = hyperparams_repository
 
-
     def train(self, pipeline: BaseStep, data_inputs, expected_outputs=None, context: ExecutionContext = None) -> Trial:
         """
         Train pipeline using the validation splitter.
@@ -628,12 +631,14 @@ def fit_trial_split(
 
         for i in range(self.epochs):
             context.logger.info('epoch {}/{}'.format(i + 1, self.epochs))
-            trial_split = trial_split.fit_trial_split(train_data_container.copy(), 
+            trial_split = trial_split.fit_trial_split(train_data_container.copy(),
                                                       context.copy().set_execution_phase(ExecutionPhase.TRAIN))
-            y_pred_train = trial_split.predict_with_pipeline(train_data_container.copy(), 
-                                                             context.copy().set_execution_phase(ExecutionPhase.VALIDATION))
+            y_pred_train = trial_split.predict_with_pipeline(train_data_container.copy(),
+                                                             context.copy().set_execution_phase(
+                                                                 ExecutionPhase.VALIDATION))
             y_pred_val = trial_split.predict_with_pipeline(validation_data_container.copy(),
-                                                           context.copy().set_execution_phase(ExecutionPhase.VALIDATION))
+                                                           context.copy().set_execution_phase(
+                                                               ExecutionPhase.VALIDATION))
 
             if self.callbacks.call(
                     trial_split=trial_split,
@@ -643,7 +648,8 @@ def fit_trial_split(
                     pred_train=y_pred_train,
                     input_val=validation_data_container,
                     pred_val=y_pred_val,
-                    is_finished_and_fitted=False
+                    context=context.copy().set_execution_phase(ExecutionPhase.VALIDATION),
+                    is_finished_and_fitted=False,
             ):
                 break
             # Saves the metrics
@@ -728,7 +734,7 @@ def __init__(
             callbacks: List[BaseCallback] = None,
             refit_scoring_function: Callable = None,
             cache_folder_when_no_handle=None,
-            n_jobs=-1,
+            n_jobs=None,
             continue_loop_on_error=True
     ):
         """
@@ -749,7 +755,7 @@ def __init__(
         :param callbacks: A list of callbacks to perform after each epoch.
         :param refit_scoring_function: A scoring function to use on a refit call
         :param cache_folder_when_no_handle: default cache folder used if auto_ml_loop isn't called through handler functions.
-        :param n_jobs: If n_jobs in (-1, None, 1), then automl is executed in a single thread. if n_jobs > 1, then n_jobs thread are launched, if n_jobs < -1 then (n_cpus + 1 + n_jobs) thread are launched.
+        :param n_jobs: If n_jobs in (None, 1), then automl is executed in a single process, which may spawns on multiple thread. if n_jobs > 1, then n_jobs process are launched, if n_jobs <= -1 then (n_cpus + 1 + n_jobs) process are launched. One trial at a time is executed by process.
         :param continue_loop_on_error:
         """
         BaseStep.__init__(self)
@@ -757,7 +763,6 @@ def __init__(
 
         self.validation_splitter: BaseValidationSplitter = validation_splitter
 
-
         if hyperparams_optimizer is None:
             hyperparams_optimizer = RandomSearchHyperparameterSelectionStrategy()
         self.hyperparameter_optimizer: BaseHyperparameterSelectionStrategy = hyperparams_optimizer
@@ -806,20 +811,27 @@ def _fit_data_container(self, data_container: DataContainer, context: ExecutionC
         # Keeping a reference of the main logger
         main_logger = context.logger
 
-        if self.n_jobs in (-1, None, 1):
+        if self.n_jobs in (None, 1):
+            # Single Process
             for trial_number in range(self.n_trial):
                 self._attempt_trial(trial_number, validation_splits, context)
         else:
+            # Multiprocssing
             context.logger.info(f"Number of processors available: {multiprocessing.cpu_count()}")
 
             if isinstance(self.hyperparams_repository, InMemoryHyperparamsRepository):
-                raise ValueError("Cannot use InMemoryHyperparamsRepository for multiprocessing, use json-based repository.")
+                raise ValueError(
+                    "Cannot use InMemoryHyperparamsRepository for multiprocessing, use json-based repository.")
 
             n_jobs = self.n_jobs
-            if n_jobs < -1:
+            if n_jobs <= -1:
                 n_jobs = multiprocessing.cpu_count() + 1 + self.n_jobs
 
             with multiprocessing.get_context("spawn").Pool(processes=n_jobs) as pool:
+                # Note : In python 3.6, the root logger possess a lock which make it non-pickable.
+                # The following line is not necessary in Python 3.7 and 3.8.
+                context.set_logger(None)
+
                 args = [(self, trial_number, validation_splits, context) for trial_number in range(self.n_trial)]
                 pool.starmap(AutoML._attempt_trial, args)
 
@@ -886,6 +898,9 @@ def _attempt_trial(self, trial_number, validation_splits, context: ExecutionCont
             context.logger.error(track)
         finally:
             repo_trial.update_final_trial_status()
+            # Some heavy objects might have stayed in memory for a while during the execution of our trial;
+            # It is best to do a full collection as that may free up some ram.
+            gc.collect()
 
     def _fit_transform_data_container(self, data_container: DataContainer, context: ExecutionContext) -> \
             ('BaseStep', DataContainer):
diff --git a/neuraxle/metaopt/callbacks.py b/neuraxle/metaopt/callbacks.py
index 685a873b..68f13a6b 100644
--- a/neuraxle/metaopt/callbacks.py
+++ b/neuraxle/metaopt/callbacks.py
@@ -28,6 +28,7 @@
 from abc import ABC, abstractmethod
 from typing import Callable
 
+from neuraxle.base import ExecutionContext
 from neuraxle.data_container import DataContainer
 from neuraxle.metaopt.trial import TrialSplit
 
@@ -56,7 +57,7 @@ class BaseCallback(ABC):
 
     @abstractmethod
     def call(self, trial_split: TrialSplit, epoch_number: int, total_epochs: int, input_train: DataContainer,
-             pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer,
+             pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer, context: ExecutionContext,
              is_finished_and_fitted: bool):
         pass
 
@@ -82,8 +83,14 @@ class EarlyStoppingCallback(BaseCallback):
         :class:`~neuraxle.data_container.DataContainer`
     """
 
-    def __init__(self, max_epochs_without_improvement):
+    def __init__(self, max_epochs_without_improvement, metric_name=None):
+        """
+
+        :param max_epochs_without_improvement: The number of step without improvement on the validation score before an early stopping is triggered.
+        :param metric_name: The name of the metric on which we want to condition the early stopping. If None, the main metric will be used.
+        """
         self.n_epochs_without_improvement = max_epochs_without_improvement
+        self.metric_name = None
 
     def call(
             self,
@@ -94,9 +101,14 @@ def call(
             pred_train: DataContainer,
             input_val: DataContainer,
             pred_val: DataContainer,
+            context: ExecutionContext,
             is_finished_and_fitted: bool
     ):
-        validation_scores = trial_split.get_validation_scores()
+        if self.metric_name is None:
+            validation_scores = trial_split.get_validation_scores()
+        else :
+            validation_scores = trial_split.get_metric_validation_results(self.metric_name)
+
         if len(validation_scores) > self.n_epochs_without_improvement:
             higher_score_is_better = trial_split.is_higher_score_better()
             if (higher_score_is_better) and \
@@ -144,6 +156,7 @@ def call(
             pred_train: DataContainer,
             input_val: DataContainer,
             pred_val: DataContainer,
+            context: ExecutionContext,
             is_finished_and_fitted: bool
     ):
         pass
@@ -171,7 +184,7 @@ class IfBestScore(MetaCallback):
     """
 
     def call(self, trial_split: TrialSplit, epoch_number: int, total_epochs: int, input_train: DataContainer,
-             pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer,
+             pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer, context: ExecutionContext,
              is_finished_and_fitted: bool):
         if trial_split.is_new_best_score():
             if self.wrapped_callback.call(
@@ -182,6 +195,7 @@ def call(self, trial_split: TrialSplit, epoch_number: int, total_epochs: int, in
                     pred_train,
                     input_val,
                     pred_val,
+                    context,
                     is_finished_and_fitted
             ):
                 return True
@@ -209,7 +223,7 @@ class IfLastStep(MetaCallback):
     """
 
     def call(self, trial_split: TrialSplit, epoch_number: int, total_epochs: int, input_train: DataContainer,
-             pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer,
+             pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer, context: ExecutionContext,
              is_finished_and_fitted: bool):
         if epoch_number == total_epochs - 1 or is_finished_and_fitted:
             self.wrapped_callback.call(
@@ -220,6 +234,7 @@ def call(self, trial_split: TrialSplit, epoch_number: int, total_epochs: int, in
                 pred_train,
                 input_val,
                 pred_val,
+                context,
                 is_finished_and_fitted
             )
             return True
@@ -247,7 +262,7 @@ def __init__(self, label):
         self.label = label
 
     def call(self, trial_split: TrialSplit, epoch_number: int, total_epochs: int, input_train: DataContainer,
-             pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer,
+             pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer, context: ExecutionContext,
              is_finished_and_fitted: bool):
         trial_split.save_model(self.label)
         return False
@@ -290,7 +305,7 @@ def __getitem__(self, item):
         return self.callbacks[item]
 
     def call(self, trial_split: TrialSplit, epoch_number: int, total_epochs: int, input_train: DataContainer,
-             pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer,
+             pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer, context: ExecutionContext,
              is_finished_and_fitted: bool):
         is_finished_and_fitted = False
         for callback in self.callbacks:
@@ -303,6 +318,7 @@ def call(self, trial_split: TrialSplit, epoch_number: int, total_epochs: int, in
                         pred_train=pred_train,
                         input_val=input_val,
                         pred_val=pred_val,
+                        context=context,
                         is_finished_and_fitted=is_finished_and_fitted
                 ):
                     is_finished_and_fitted = True
@@ -337,17 +353,24 @@ class MetricCallback(BaseCallback):
         :class:`~neuraxle.data_container.DataContainer`
     """
 
-    def __init__(self, name: str, metric_function: Callable, higher_score_is_better: bool, log_metrics=True):
+    def __init__(self, name: str, metric_function: Callable, higher_score_is_better: bool, log_metrics=True,
+                 pass_context_to_metric_function: bool = False):
         self.name = name
         self.metric_function = metric_function
         self.higher_score_is_better = higher_score_is_better
         self.log_metrics = log_metrics
+        self.pass_context_to_metric_function = pass_context_to_metric_function
 
     def call(self, trial_split: TrialSplit, epoch_number: int, total_epochs: int, input_train: DataContainer,
-             pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer,
+             pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer, context: ExecutionContext,
              is_finished_and_fitted: bool):
-        train_score = self.metric_function(pred_train.expected_outputs, pred_train.data_inputs)
-        validation_score = self.metric_function(pred_val.expected_outputs, pred_val.data_inputs)
+
+        if self.pass_context_to_metric_function:
+            train_score = self.metric_function(pred_train.expected_outputs, pred_train.data_inputs, context=context)
+            validation_score = self.metric_function(pred_val.expected_outputs, pred_val.data_inputs, context=context)
+        else:
+            train_score = self.metric_function(pred_train.expected_outputs, pred_train.data_inputs)
+            validation_score = self.metric_function(pred_val.expected_outputs, pred_val.data_inputs)
 
         trial_split.add_metric_results_train(
             name=self.name,
@@ -389,12 +412,12 @@ class ScoringCallback(MetricCallback):
         :class:`~neuraxle.base.HyperparameterSamples`,
         :class:`~neuraxle.data_container.DataContainer`
     """
-
     def __init__(self, metric_function: Callable, name='main', higher_score_is_better: bool = True,
-                 log_metrics: bool = True):
+                 log_metrics: bool = True, pass_context_to_metric_function: bool = False):
         super().__init__(
             name=name,
             metric_function=metric_function,
             higher_score_is_better=higher_score_is_better,
-            log_metrics=log_metrics
+            log_metrics=log_metrics,
+            pass_context_to_metric_function=pass_context_to_metric_function
         )
diff --git a/neuraxle/metaopt/random.py b/neuraxle/metaopt/random.py
index 658d4e32..82b0c577 100644
--- a/neuraxle/metaopt/random.py
+++ b/neuraxle/metaopt/random.py
@@ -206,12 +206,6 @@ def _fit_transform_data_container(self, data_container: DataContainer, context:
         """
         train_data_container, validation_data_container = self.split_data_container(data_container)
 
-        # add sub data container for the validation metrics calculated in MetricsWrapper
-        train_data_container.add_sub_data_container(
-            name=VALIDATION_SUB_DATA_CONTAINER_NAME,
-            data_container=validation_data_container
-        )
-
         self.wrapped, results_data_container = self.wrapped.handle_fit_transform(train_data_container,
                                                                                  context.push(self.wrapped))
 
diff --git a/neuraxle/pipeline.py b/neuraxle/pipeline.py
index eddec2b7..919db64a 100644
--- a/neuraxle/pipeline.py
+++ b/neuraxle/pipeline.py
@@ -147,14 +147,12 @@ def _fit_data_container(self, data_container: DataContainer, context: ExecutionC
         :return: tuple(pipeline, data_container)
         """
         steps_left_to_do, data_container = self._load_checkpoint(data_container, context)
-        self.setup(context=context)
 
         index_last_step = len(steps_left_to_do) - 1
 
         new_steps_as_tuple: NamedTupleList = []
 
         for index, (step_name, step) in enumerate(steps_left_to_do):
-            step.setup(context=context)
 
             if index != index_last_step:
                 step, data_container = step.handle_fit_transform(data_container, context)
@@ -178,12 +176,10 @@ def _fit_transform_data_container(self, data_container: DataContainer, context:
         :return: tuple(pipeline, data_container)
         """
         steps_left_to_do, data_container = self._load_checkpoint(data_container, context)
-        self.setup(context=context)
 
         new_steps_as_tuple: NamedTupleList = []
 
         for step_name, step in steps_left_to_do:
-            step.setup(context=context)
             step, data_container = step.handle_fit_transform(data_container, context)
             new_steps_as_tuple.append((step_name, step))
 
@@ -222,16 +218,6 @@ def _load_checkpoint(self, data_container: DataContainer, context: ExecutionCont
     def flush_all_cache(self):
         shutil.rmtree(self.cache_folder)
 
-    def setup(self, context: ExecutionContext = None) -> 'Pipeline':
-        """
-        Contrary to the default behaviour of TruncableStep, we don't want to recursively call .setup() in a Pipeline instance.
-        We'll call the setup before calling each steps on a fit call.
-        :param context:
-        :return:
-        """
-        self.is_initialized = True
-        return self
-
 
 class ResumablePipeline(ResumableStepMixin, Pipeline):
     """
diff --git a/neuraxle/steps/column_transformer.py b/neuraxle/steps/column_transformer.py
index 9444b9c3..9554059f 100644
--- a/neuraxle/steps/column_transformer.py
+++ b/neuraxle/steps/column_transformer.py
@@ -130,7 +130,7 @@ class ColumnsSelectorND(MetaStep):
     def __init__(self, columns_selection, n_dimension=3):
         assert n_dimension >= 2
         col_selector: ColumnSelector2D = ColumnSelector2D(columns_selection=columns_selection)
-        for _ in range(min(0, n_dimension - 2)):
+        for _ in range(max(0, n_dimension - 2)):
             col_selector = ForEach(col_selector)
 
         MetaStep.__init__(self, col_selector)
diff --git a/neuraxle/steps/data.py b/neuraxle/steps/data.py
index 4661d4d0..59adde9d 100644
--- a/neuraxle/steps/data.py
+++ b/neuraxle/steps/data.py
@@ -20,7 +20,8 @@
 
 """
 import random
-from typing import Iterable
+from operator import attrgetter
+from typing import Iterable, List
 
 from neuraxle.base import BaseStep, MetaStep, ExecutionContext, ForceHandleOnlyMixin, BaseTransformer
 from neuraxle.data_container import DataContainer, _inner_concatenate_np_array
@@ -259,7 +260,7 @@ def _transform_data_container(self, data_container: DataContainer, context: Exec
         """
         return self._concatenate_sub_data_containers(data_container)
 
-    def _concatenate_sub_data_containers(self, data_container: DataContainer):
+    def _concatenate_sub_data_containers(self, data_container: DataContainer) -> DataContainer:
         """
         Merge sub data containers into the current data container.
 
@@ -268,7 +269,7 @@ def _concatenate_sub_data_containers(self, data_container: DataContainer):
         :return: base step, data container
         :rtype: DataContainer
         """
-        sub_data_containers_to_zip = []
+        sub_data_containers_to_zip = [data_container]
         if self.data_sources is None:
             self.data_sources = data_container.get_sub_data_container_names()
 
@@ -276,12 +277,12 @@ def _concatenate_sub_data_containers(self, data_container: DataContainer):
             if name in self.data_sources:
                 sub_data_containers_to_zip.append(sub_data_container)
 
-        for data_container_to_zip in sub_data_containers_to_zip:
-            data_container = self._concatenate_sub_data_container(data_container, data_container_to_zip)
+        if len(sub_data_containers_to_zip) > 1:
+            data_container = self._concatenate_sub_data_container(sub_data_containers_to_zip)
 
         return data_container
 
-    def _concatenate_sub_data_container(self, data_container, data_container_to_zip) -> DataContainer:
+    def _concatenate_sub_data_container(self, data_container_to_zip: List[DataContainer]) -> DataContainer:
         """
         Zip a data container into another data container with a higher dimension.
 
@@ -291,19 +292,19 @@ def _concatenate_sub_data_container(self, data_container, data_container_to_zip)
         :type data_container_to_zip: DataContainer
         :return: concatenated data containers
         """
-        data_inputs = _inner_concatenate_np_array(data_container.data_inputs,
-                                                  data_container_to_zip.data_inputs)
-        data_container.set_data_inputs(data_inputs)
+        data_inputs = _inner_concatenate_np_array(list(map(attrgetter("data_inputs"), data_container_to_zip)))
 
-        expected_outputs = _inner_concatenate_np_array(data_container.expected_outputs,
-                                                       data_container_to_zip.expected_outputs)
-        data_container.set_expected_outputs(expected_outputs)
+        expected_outputs = _inner_concatenate_np_array(list(map(attrgetter("expected_outputs"), data_container_to_zip)))
 
-        return data_container
+        return data_container_to_zip[0].copy()\
+            .set_data_inputs(data_inputs)\
+            .set_expected_outputs(expected_outputs)
 
 
 class ZipBatchDataContainer(ForceHandleOnlyMixin, BaseTransformer):
     """
+    WARNING: Unexpected behaviour from this class. It's not to date.
+
     Concatenate outer batch of sub data containers along `axis=0`..
 
     Code example:
diff --git a/neuraxle/steps/flow.py b/neuraxle/steps/flow.py
index 667b0d18..4f597f13 100644
--- a/neuraxle/steps/flow.py
+++ b/neuraxle/steps/flow.py
@@ -23,6 +23,7 @@
     project, visit https://www.umaneo.com/ for more information on Umaneo Technologies Inc.
 
 """
+from operator import attrgetter
 from typing import Union, Optional as OptionalType, Dict, Callable
 
 from neuraxle.base import BaseStep, MetaStep, DataContainer, ExecutionContext, TruncableSteps, ResumableStepMixin, \
@@ -375,7 +376,7 @@ class ChooseOneStepOf(FeatureUnion):
     """
 
     def __init__(self, steps, hyperparams=None):
-        FeatureUnion.__init__(self, steps, joiner=SelectNonEmptyDataInputs())
+        FeatureUnion.__init__(self, steps, joiner=SelectNonEmptyDataContainer())
 
         self._make_all_steps_optional()
 
@@ -406,7 +407,7 @@ def set_hyperparams(self, hyperparams: Union[HyperparameterSamples, dict]):
 
         return self
 
-    def update_hyperparams(self, hyperparams: Union[HyperparameterSamples, dict]):
+    def _update_hyperparams(self, hyperparams: Union[HyperparameterSamples, dict]):
         """
         Set chosen step hyperparams.
 
@@ -414,16 +415,14 @@ def update_hyperparams(self, hyperparams: Union[HyperparameterSamples, dict]):
         :type hyperparams: HyperparameterSamples
         :return:
         """
-        super().update_hyperparams(hyperparams)
+        super()._update_hyperparams(hyperparams)
         self._update_optional_hyperparams()
-
-        return self
+        return self.hyperparams
 
     def _update_optional_hyperparams(self):
         step_names = list(self.keys())
-        chosen_step_name = self.hyperparams[self.CHOICE_HYPERPARAM] if self.CHOICE_HYPERPARAM in self.hyperparams else \
-        step_names[
-            0]
+        chosen_step_name = self.hyperparams[self.CHOICE_HYPERPARAM] if self.CHOICE_HYPERPARAM in self.hyperparams \
+            else step_names[0]
 
         if chosen_step_name not in step_names:
             raise ValueError('Invalid Chosen Step in {0}'.format(self.name))
@@ -527,10 +526,34 @@ def _transform_data_container(self, data_container: DataContainer, context: Exec
 
         data_container = DataContainer(data_inputs=data_inputs, current_ids=data_container.current_ids,
                                        expected_outputs=data_container.expected_outputs)
-        data_container.set_data_inputs(data_inputs)
 
         return data_container
 
+class SelectNonEmptyDataContainer(TransformHandlerOnlyMixin, BaseTransformer):
+    """
+    A step that selects non empty data containers.
+    Assumes that the given DataContainer contains a list of DataContainer as data_inputs.
+
+    .. seealso::
+        :class:`~neuraxle.base.TransformHandlerOnlyMixin`,
+        :class:`~neuraxle.base.BaseStep`
+    """
+
+    def __init__(self):
+        BaseTransformer.__init__(self)
+        TransformHandlerOnlyMixin.__init__(self)
+
+    def _transform_data_container(self, data_container: DataContainer, context: ExecutionContext):
+
+        data_containers = list(filter(lambda dc: (len(dc.data_inputs) > 0 and len(dc.expected_outputs) > 0),
+                                      data_container.data_inputs))
+        if len(data_containers) == 1:
+            return data_containers[0]
+        else:
+            return DataContainer(data_inputs=list(map(attrgetter("data_inputs"))),
+                                 expected_outputs=list(map(attrgetter("expected_outputs"))),
+                                 current_ids=data_container.current_ids)
+
 
 class ExpandDim(ResumableStepMixin, MetaStep):
     """
diff --git a/neuraxle/steps/sklearn.py b/neuraxle/steps/sklearn.py
index ae8ada07..8c334828 100644
--- a/neuraxle/steps/sklearn.py
+++ b/neuraxle/steps/sklearn.py
@@ -46,7 +46,7 @@ def __init__(
             return_all_sklearn_default_params_on_get: bool = False,
             use_partial_fit: bool = False,
             use_predict_proba: bool = False,
-            partial_fit_kwargs: dict = {}
+            partial_fit_kwargs: dict = None
     ):
         if not isinstance(wrapped_sklearn_predictor, BaseEstimator):
             raise ValueError("The wrapped_sklearn_predictor must be an instance of scikit-learn's BaseEstimator.")
@@ -59,6 +59,8 @@ def __init__(
         self.name += "_" + wrapped_sklearn_predictor.__class__.__name__
         self.use_partial_fit: bool = use_partial_fit
         if self.use_partial_fit:
+            if partial_fit_kwargs is None:
+                partial_fit_kwargs = {}
             self.partial_fit_kwargs = partial_fit_kwargs
         self.use_predict_proba: bool = use_predict_proba
 


Test patch:
diff --git a/testing/steps/test_choose_one_or_many_steps_of.py b/testing/steps/test_choose_one_or_many_steps_of.py
index 4bc9ba42..200a18d7 100644
--- a/testing/steps/test_choose_one_or_many_steps_of.py
+++ b/testing/steps/test_choose_one_or_many_steps_of.py
@@ -433,3 +433,77 @@ def test_choose_one_step_of_invalid_chosen_step():
                 ('b', Identity())
             ]).set_hyperparams({'choice': 'c'}),
         ])
+
+@pytest.mark.parametrize("method_name, args, kwargs", [
+    ("set_hyperparams", [{'choice': 'b'}], {}),
+    ("update_hyperparams", [{'choice': 'b'}], {}),
+    ("apply", ["_update_hyperparams"], {"hyperparams":{'choice': 'b'}}) # This case correspond to the update in an AutoML loop.
+])
+def test_choose_one_step_of_set_hyperparams(method_name, args, kwargs):
+    a_callback = TapeCallbackFunction()
+    b_callback = TapeCallbackFunction()
+    c_callback = TapeCallbackFunction()
+    d_callback = TapeCallbackFunction()
+
+    choose_one_step_of = ChooseOneStepOf([
+            ('a', FitTransformCallbackStep(a_callback, c_callback, transform_function=lambda di: di * 2).set_name("step_1")),
+            ('b', FitTransformCallbackStep(b_callback, d_callback, transform_function=lambda di: di * 2).set_name("step_1"))
+        ])
+
+    p = Pipeline([
+        choose_one_step_of
+    ])
+
+    p.transform(DATA_INPUTS)
+
+    assert len(a_callback.data) == 1
+    assert all(a_callback.data[0] == DATA_INPUTS)
+    assert len(b_callback.data) == 0
+    assert len(c_callback.data) == 0
+    assert len(d_callback.data) == 0
+
+    getattr(choose_one_step_of, method_name)(*args, **kwargs)
+
+    p.transform(DATA_INPUTS)
+
+    assert len(a_callback.data) == 1
+    assert all(a_callback.data[0] == DATA_INPUTS)
+    assert len(b_callback.data) == 1
+    assert all(b_callback.data[0] == DATA_INPUTS)
+    assert len(c_callback.data) == 0
+    assert len(d_callback.data) == 0
+
+
+def test_choose_one_step_of_update_hyperparams():
+    a_callback = TapeCallbackFunction()
+    b_callback = TapeCallbackFunction()
+    c_callback = TapeCallbackFunction()
+    d_callback = TapeCallbackFunction()
+
+    choose_one_step_of = ChooseOneStepOf([
+            ('a', FitTransformCallbackStep(a_callback, c_callback, transform_function=lambda di: di * 2).set_name("step_1")),
+            ('b', FitTransformCallbackStep(b_callback, d_callback, transform_function=lambda di: di * 2).set_name("step_1"))
+        ])
+
+    p = Pipeline([
+        choose_one_step_of
+    ])
+
+    p.transform(DATA_INPUTS)
+
+    assert len(a_callback.data) == 1
+    assert all(a_callback.data[0] == DATA_INPUTS)
+    assert len(b_callback.data) == 0
+    assert len(c_callback.data) == 0
+    assert len(d_callback.data) == 0
+
+    choose_one_step_of.update_hyperparams({'choice': 'b'})
+
+    p.transform(DATA_INPUTS)
+
+    assert len(a_callback.data) == 1
+    assert all(a_callback.data[0] == DATA_INPUTS)
+    assert len(b_callback.data) == 1
+    assert all(b_callback.data[0] == DATA_INPUTS)
+    assert len(c_callback.data) == 0
+    assert len(d_callback.data) == 0
diff --git a/testing/test_column_selector_2d.py b/testing/steps/test_column_selector_2d.py
similarity index 98%
rename from testing/test_column_selector_2d.py
rename to testing/steps/test_column_selector_2d.py
index 0125faf6..0705d8df 100644
--- a/testing/test_column_selector_2d.py
+++ b/testing/steps/test_column_selector_2d.py
@@ -55,7 +55,7 @@ def test_column_selector_2d_should_throw_exception_on_unsupported_type(column_se
 
 
 def test_column_selector_nd_should_transform_with_column_selector_2d():
-    step = ColumnsSelectorND(0)
+    step = ColumnsSelectorND(0, n_dimension=2)
     data_inputs, expected_outputs = _create_data_source((20, 20))
 
     outputs = step.transform(data_inputs)
diff --git a/testing/steps/test_column_transformer.py b/testing/steps/test_column_transformer.py
index dac12236..a185f08b 100644
--- a/testing/steps/test_column_transformer.py
+++ b/testing/steps/test_column_transformer.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 import pytest
+
 from neuraxle.base import BaseStep
 from neuraxle.steps.column_transformer import ColumnTransformer
 
@@ -24,7 +25,7 @@ def _add_fitted_data(self, data_inputs, expected_outputs):
         self.fitted_data.append((copy.deepcopy(data_inputs), copy.deepcopy(expected_outputs)))
 
     def transform(self, data_inputs):
-        return data_inputs * 2
+        return (2 * np.array(data_inputs)).tolist()
 
 
 class ColumnChooserTestCase:
@@ -47,7 +48,8 @@ def __init__(
         self.column_transformer_tuple_list = column_transformer_tuple_list
 
 
-test_case_index_int = ColumnChooserTestCase(
+# 2d
+test_case_index_int_2d = ColumnChooserTestCase(
     data_inputs=np.array([
         [0, 1, 2, 3],
         [10, 11, 12, 13],
@@ -71,10 +73,10 @@ def __init__(
     column_transformer_tuple_list=[
         (1, MultiplyBy2())
     ],
-    n_dimension=3
+    n_dimension=2
 )
 
-test_case_index_start_end = ColumnChooserTestCase(
+test_case_index_start_end_2d = ColumnChooserTestCase(
     data_inputs=np.array([
         [0, 1, 2, 3],
         [10, 11, 12, 13],
@@ -98,10 +100,10 @@ def __init__(
     column_transformer_tuple_list=[
         (slice(0, 2), MultiplyBy2())
     ],
-    n_dimension=3
+    n_dimension=2
 )
 
-test_case_index_range = ColumnChooserTestCase(
+test_case_index_range_2d = ColumnChooserTestCase(
     data_inputs=np.array([
         [0, 1, 2, 3],
         [10, 11, 12, 13],
@@ -125,10 +127,10 @@ def __init__(
     column_transformer_tuple_list=[
         (range(2), MultiplyBy2())
     ],
-    n_dimension=3
+    n_dimension=2
 )
 
-test_case_index_start = ColumnChooserTestCase(
+test_case_index_start_2d = ColumnChooserTestCase(
     data_inputs=np.array([
         [0, 1, 2, 3],
         [10, 11, 12, 13],
@@ -152,10 +154,10 @@ def __init__(
     column_transformer_tuple_list=[
         (slice(1, None), MultiplyBy2())
     ],
-    n_dimension=3
+    n_dimension=2
 )
 
-test_case_index_end = ColumnChooserTestCase(
+test_case_index_end_2d = ColumnChooserTestCase(
     data_inputs=np.array([
         [0, 1, 2, 3],
         [10, 11, 12, 13],
@@ -179,10 +181,10 @@ def __init__(
     column_transformer_tuple_list=[
         (slice(None, 2), MultiplyBy2())
     ],
-    n_dimension=3
+    n_dimension=2
 )
 
-test_case_index_last = ColumnChooserTestCase(
+test_case_index_last_2d = ColumnChooserTestCase(
     data_inputs=np.array([
         [0, 1, 2, 3],
         [10, 11, 12, 13],
@@ -208,10 +210,10 @@ def __init__(
     column_transformer_tuple_list=[
         (slice(None, -1), MultiplyBy2())
     ],
-    n_dimension=3
+    n_dimension=2
 )
 
-test_case_list_of_columns = ColumnChooserTestCase(
+test_case_list_of_columns_2d = ColumnChooserTestCase(
     data_inputs=np.array([
         [0, 1, 2, 3],
         [10, 11, 12, 13],
@@ -235,6 +237,248 @@ def __init__(
     column_transformer_tuple_list=[
         ([0, 2], MultiplyBy2())
     ],
+    n_dimension=2
+)
+
+# 3d
+test_case_index_int = ColumnChooserTestCase(
+    data_inputs=np.array([
+        [[0, 1, 2, 3]],
+        [[10, 11, 12, 13]],
+        [[20, 21, 22, 23]]
+    ]),
+    expected_outputs=np.array([
+        [[0, 1, 2, 3]],
+        [[10, 11, 12, 13]],
+        [[20, 21, 22, 23]]
+    ]),
+    expected_processed_outputs=np.array([
+        [[2]],
+        [[22]],
+        [[42]]
+    ]),
+    expected_fitted_data=[
+        ([np.array([[1]]), np.array([[11]]), np.array([[21]])],
+         [np.array([[0, 1, 2, 3]]),
+          np.array([[10, 11, 12, 13]]),
+          np.array([[20, 21, 22, 23]])]
+         )
+    ],
+    expected_step_key='1_MultiplyBy2',
+    column_transformer_tuple_list=[
+        (1, MultiplyBy2())
+    ],
+    n_dimension=3
+)
+
+# test_case_index_int = ColumnChooserTestCase(
+#     data_inputs=np.array([[
+#         [0, 1, 2, 3],
+#         [10, 11, 12, 13],
+#         [20, 21, 22, 23]
+#     ]]),
+#     expected_outputs=np.array([[
+#         [0, 1, 2, 3],
+#         [10, 11, 12, 13],
+#         [20, 21, 22, 23]
+#     ]]),
+#     expected_processed_outputs=np.array([[
+#         [2],
+#         [22],
+#         [42]
+#     ]]),
+#     expected_fitted_data=[(
+#         [np.array([[1], [11], [21]])],
+#         [np.array([[0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23]])]
+#     )],
+#     expected_step_key='1_MultiplyBy2',
+#     column_transformer_tuple_list=[
+#         (1, MultiplyBy2())
+#     ],
+#     n_dimension=3
+# )
+
+test_case_index_start_end = ColumnChooserTestCase(
+    data_inputs=np.array([
+        [[0, 1, 2, 3]],
+        [[10, 11, 12, 13]],
+        [[20, 21, 22, 23]]
+    ]),
+    expected_outputs=np.array([
+        [[0, 1, 2, 3]],
+        [[10, 11, 12, 13]],
+        [[20, 21, 22, 23]]
+    ]),
+    expected_processed_outputs=np.array([
+        [[0, 2]],
+        [[20, 22]],
+        [[40, 42]]
+    ]),
+    expected_fitted_data=[(
+        [np.array([[0, 1]]), np.array([[10, 11]]), np.array([[20, 21]])],
+        [np.array([[0, 1, 2, 3]]),
+         np.array([[10, 11, 12, 13]]),
+         np.array([[20, 21, 22, 23]])]
+    )],
+    expected_step_key='slice(0, 2, None)_MultiplyBy2',
+    column_transformer_tuple_list=[
+        (slice(0, 2), MultiplyBy2())
+    ],
+    n_dimension=3
+)
+
+test_case_index_range = ColumnChooserTestCase(
+    data_inputs=np.array([
+        [[0, 1, 2, 3]],
+        [[10, 11, 12, 13]],
+        [[20, 21, 22, 23]]
+    ]),
+    expected_outputs=np.array([
+        [[0, 1, 2, 3]],
+        [[10, 11, 12, 13]],
+        [[20, 21, 22, 23]]
+    ]),
+    expected_processed_outputs=np.array([
+        [[0, 2]],
+        [[20, 22]],
+        [[40, 42]]
+    ]),
+    expected_fitted_data=[(
+        [np.array([[0, 1]]), np.array([[10, 11]]), np.array([[20, 21]])],
+        [np.array([[0, 1, 2, 3]]),
+         np.array([[10, 11, 12, 13]]),
+         np.array([[20, 21, 22, 23]])]
+    )],
+    expected_step_key='range(0, 2)_MultiplyBy2',
+    column_transformer_tuple_list=[
+        (range(2), MultiplyBy2())
+    ],
+    n_dimension=3
+)
+
+test_case_index_start = ColumnChooserTestCase(
+    data_inputs=np.array([
+        [[0, 1, 2, 3]],
+        [[10, 11, 12, 13]],
+        [[20, 21, 22, 23]]
+    ]),
+    expected_outputs=np.array([
+        [[0, 1, 2, 3]],
+        [[10, 11, 12, 13]],
+        [[20, 21, 22, 23]]
+    ]),
+    expected_processed_outputs=np.array([
+        [[2, 4, 6]],
+        [[22, 24, 26]],
+        [[42, 44, 46]]
+    ]),
+    expected_fitted_data=[(
+        [np.array([[1, 2, 3]]),
+         np.array([[11, 12, 13]]),
+         np.array([[21, 22, 23]])],
+        [np.array([[0, 1, 2, 3]]),
+         np.array([[10, 11, 12, 13]]),
+         np.array([[20, 21, 22, 23]])]
+    )],
+    expected_step_key='slice(1, None, None)_MultiplyBy2',
+    column_transformer_tuple_list=[
+        (slice(1, None), MultiplyBy2())
+    ],
+    n_dimension=3
+)
+
+test_case_index_end = ColumnChooserTestCase(
+    data_inputs=np.array([
+        [[0, 1, 2, 3]],
+        [[10, 11, 12, 13]],
+        [[20, 21, 22, 23]]
+    ]),
+    expected_outputs=np.array([
+        [[0, 1, 2, 3]],
+        [[10, 11, 12, 13]],
+        [[20, 21, 22, 23]]
+    ]),
+    expected_processed_outputs=np.array([
+        [[0, 2]],
+        [[20, 22]],
+        [[40, 42]]
+    ]),
+    expected_fitted_data=[(
+        [np.array([[0, 1]]),
+         np.array([[10, 11]]),
+         np.array([[20, 21]])],
+        [np.array([[0, 1, 2, 3]]),
+         np.array([[10, 11, 12, 13]]),
+         np.array([[20, 21, 22, 23]])]
+    )],
+    expected_step_key='slice(None, 2, None)_MultiplyBy2',
+    column_transformer_tuple_list=[
+        (slice(None, 2), MultiplyBy2())
+    ],
+    n_dimension=3
+)
+
+test_case_index_last = ColumnChooserTestCase(
+    data_inputs=np.array([
+        [[0, 1, 2, 3]],
+        [[10, 11, 12, 13]],
+        [[20, 21, 22, 23]]
+    ]),
+    expected_outputs=np.array([
+        [[0, 1, 2, 3]],
+        [[10, 11, 12, 13]],
+        [[20, 21, 22, 23]]
+    ]),
+    expected_processed_outputs=np.array([
+        [[0, 2, 4]],
+        [[20, 22, 24]],
+        [[40, 42, 44]]
+    ]),
+    expected_fitted_data=[
+        (
+            [np.array([[0, 1, 2]]),
+             np.array([[10, 11, 12]]),
+             np.array([[20, 21, 22]])],
+            [np.array([[0, 1, 2, 3]]),
+             np.array([[10, 11, 12, 13]]),
+             np.array([[20, 21, 22, 23]])]
+        )
+    ],
+    expected_step_key='slice(None, -1, None)_MultiplyBy2',
+    column_transformer_tuple_list=[
+        (slice(None, -1), MultiplyBy2())
+    ],
+    n_dimension=3
+)
+
+test_case_list_of_columns = ColumnChooserTestCase(
+    data_inputs=np.array([
+        [[0, 1, 2, 3]],
+        [[10, 11, 12, 13]],
+        [[20, 21, 22, 23]]
+    ]),
+    expected_outputs=np.array([
+        [[0, 1, 2, 3]],
+        [[10, 11, 12, 13]],
+        [[20, 21, 22, 23]]
+    ]),
+    expected_processed_outputs=np.array([
+        [[0, 4]],
+        [[20, 24]],
+        [[40, 44]]
+    ]),
+    expected_fitted_data=[(
+        [np.array([[0, 2]]),
+         np.array([[10, 12]]),
+         np.array([[20, 22]])],
+        [np.array([[0, 1, 2, 3]]),
+         np.array([[10, 11, 12, 13]]),
+         np.array([[20, 21, 22, 23]])]
+    )],
+    expected_step_key='[0, 2]_MultiplyBy2',
+    column_transformer_tuple_list=[
+        ([0, 2], MultiplyBy2())
+    ],
     n_dimension=3
 )
 
@@ -247,10 +491,17 @@ def __init__(
     copy.deepcopy(test_case_index_end),
     copy.deepcopy(test_case_index_last),
     copy.deepcopy(test_case_list_of_columns),
+    copy.deepcopy(test_case_index_int_2d),
+    copy.deepcopy(test_case_index_start_end_2d),
+    copy.deepcopy(test_case_index_start_2d),
+    copy.deepcopy(test_case_index_range_2d),
+    copy.deepcopy(test_case_index_end_2d),
+    copy.deepcopy(test_case_index_last_2d),
+    copy.deepcopy(test_case_list_of_columns_2d)
 ])
 def test_column_transformer_transform_should_support_indexes(test_case: ColumnChooserTestCase):
     data_inputs = test_case.data_inputs
-    column_transformer = ColumnTransformer(test_case.column_transformer_tuple_list)
+    column_transformer = ColumnTransformer(test_case.column_transformer_tuple_list, test_case.n_dimension)
 
     outputs = column_transformer.transform(data_inputs)
 
@@ -264,12 +515,19 @@ def test_column_transformer_transform_should_support_indexes(test_case: ColumnCh
     copy.deepcopy(test_case_index_range),
     copy.deepcopy(test_case_index_end),
     copy.deepcopy(test_case_index_last),
-    copy.deepcopy(test_case_list_of_columns)
+    copy.deepcopy(test_case_list_of_columns),
+    copy.deepcopy(test_case_index_int_2d),
+    copy.deepcopy(test_case_index_start_end_2d),
+    copy.deepcopy(test_case_index_start_2d),
+    copy.deepcopy(test_case_index_range_2d),
+    copy.deepcopy(test_case_index_end_2d),
+    copy.deepcopy(test_case_index_last_2d),
+    copy.deepcopy(test_case_list_of_columns_2d)
 ])
 def test_column_transformer_fit_transform_should_support_indexes(test_case: ColumnChooserTestCase):
     data_inputs = test_case.data_inputs
     expected_outputs = test_case.expected_outputs
-    p = ColumnTransformer(test_case.column_transformer_tuple_list)
+    p = ColumnTransformer(test_case.column_transformer_tuple_list, test_case.n_dimension)
 
     p, outputs = p.fit_transform(data_inputs, expected_outputs)
 
@@ -286,11 +544,18 @@ def test_column_transformer_fit_transform_should_support_indexes(test_case: Colu
     copy.deepcopy(test_case_index_range),
     copy.deepcopy(test_case_index_end),
     copy.deepcopy(test_case_index_last),
-    copy.deepcopy(test_case_list_of_columns)
+    copy.deepcopy(test_case_list_of_columns),
+    copy.deepcopy(test_case_index_int_2d),
+    copy.deepcopy(test_case_index_start_end_2d),
+    copy.deepcopy(test_case_index_start_2d),
+    copy.deepcopy(test_case_index_range_2d),
+    copy.deepcopy(test_case_index_end_2d),
+    copy.deepcopy(test_case_index_last_2d),
+    copy.deepcopy(test_case_list_of_columns_2d)
 ])
 def test_column_transformer_fit_should_support_indexes(test_case: ColumnChooserTestCase):
     data_inputs = test_case.data_inputs
-    p = ColumnTransformer(test_case.column_transformer_tuple_list)
+    p = ColumnTransformer(test_case.column_transformer_tuple_list, test_case.n_dimension)
 
     p = p.fit(data_inputs, test_case.expected_outputs)
 
@@ -303,19 +568,19 @@ def test_column_transformer_fit_should_support_multiple_tuples():
     # Given
     test_case = ColumnChooserTestCase(
         data_inputs=np.array([
-            [1, 1, 2, 3],
-            [10, 11, 12, 13],
-            [20, 21, 22, 23]
+            [[1, 1, 2, 3]],
+            [[10, 11, 12, 13]],
+            [[20, 21, 22, 23]]
         ]),
         expected_outputs=np.array([
-            [0, 1, 2, 3],
-            [10, 11, 12, 13],
-            [20, 21, 22, 23]
+            [[0, 1, 2, 3]],
+            [[10, 11, 12, 13]],
+            [[20, 21, 22, 23]]
         ]),
         expected_processed_outputs=np.array([
-            [2, 2, 2, 3],
-            [20, 22, 12, 13],
-            [40, 42, 44, 46]
+            [[2, 2, 2, 3]],
+            [[20, 22, 12, 13]],
+            [[40, 42, 44, 46]]
         ]),
         column_transformer_tuple_list=[
             (slice(0, 2), MultiplyBy2()),
@@ -324,18 +589,23 @@ def test_column_transformer_fit_should_support_multiple_tuples():
         n_dimension=3
     )
     data_inputs = test_case.data_inputs
-    p = ColumnTransformer(test_case.column_transformer_tuple_list)
+    p = ColumnTransformer(test_case.column_transformer_tuple_list, test_case.n_dimension)
 
     # When
     p = p.fit(data_inputs, test_case.expected_outputs)
 
     # Then
     actual_fitted_data = p['2_MultiplyBy2']['MultiplyBy2'].fitted_data
-    expected_fitted_data = [([[2], [12], [22]], [[0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23]])]
+    expected_fitted_data = [
+        ([np.array([[2]]), np.array([[12]]), np.array([[22]])],
+         [np.array([[0, 1, 2, 3]]), np.array([[10, 11, 12, 13]]), np.array([[20, 21, 22, 23]])])
+    ]
     assert_data_fitted_properly(actual_fitted_data, expected_fitted_data)
 
     actual_fitted_data = p['slice(0, 2, None)_MultiplyBy2']['MultiplyBy2'].fitted_data
-    expected_fitted_data = [([[1, 1], [10, 11], [20, 21]], [[0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23]])]
+    expected_fitted_data = [([np.array([[1, 1]]), np.array([[10, 11]]), np.array([[20, 21]])],
+                              [np.array([[0, 1, 2, 3]]), np.array([[10, 11, 12, 13]]), np.array([[20, 21, 22, 23]])]
+                            )]
     assert_data_fitted_properly(actual_fitted_data, expected_fitted_data)
 
 
@@ -343,19 +613,19 @@ def test_column_transformer_fit_transform_should_support_multiple_tuples():
     # Given
     test_case = ColumnChooserTestCase(
         data_inputs=np.array([
-            [1, 1, 2, 3],
-            [10, 11, 12, 13],
-            [20, 21, 22, 23]
+            [[1, 1, 2, 3]],
+            [[10, 11, 12, 13]],
+            [[20, 21, 22, 23]]
         ]),
         expected_outputs=np.array([
-            [0, 1, 2, 3],
-            [10, 11, 12, 13],
-            [20, 21, 22, 23]
+            [[0, 1, 2, 3]],
+            [[10, 11, 12, 13]],
+            [[20, 21, 22, 23]]
         ]),
         expected_processed_outputs=np.array([
-            [2, 2, 4],
-            [20, 22, 24],
-            [40, 42, 44]
+            [[2, 2, 4]],
+            [[20, 22, 24]],
+            [[40, 42, 44]]
         ]),
         column_transformer_tuple_list=[
             (slice(0, 2), MultiplyBy2()),
@@ -364,7 +634,7 @@ def test_column_transformer_fit_transform_should_support_multiple_tuples():
         n_dimension=3
     )
     data_inputs = test_case.data_inputs
-    p = ColumnTransformer(test_case.column_transformer_tuple_list)
+    p = ColumnTransformer(test_case.column_transformer_tuple_list, test_case.n_dimension)
 
     # When
     p, outputs = p.fit_transform(data_inputs, test_case.expected_outputs)
@@ -372,11 +642,17 @@ def test_column_transformer_fit_transform_should_support_multiple_tuples():
     # Then
     assert np.array_equal(test_case.expected_processed_outputs, outputs)
     actual_fitted_data = p['2_MultiplyBy2']['MultiplyBy2'].fitted_data
-    expected_fitted_data = [([[2], [12], [22]], [[0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23]])]
+    expected_fitted_data = [
+        ([np.array([[2]]), np.array([[12]]), np.array([[22]])],
+         [np.array([[0, 1, 2, 3]]), np.array([[10, 11, 12, 13]]), np.array([[20, 21, 22, 23]])])
+                            ]
     assert_data_fitted_properly(actual_fitted_data, expected_fitted_data)
 
     actual_fitted_data = p['slice(0, 2, None)_MultiplyBy2']['MultiplyBy2'].fitted_data
-    expected_fitted_data = [([[1, 1], [10, 11], [20, 21]], [[0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23]])]
+    expected_fitted_data = [
+        ([np.array([[1, 1]]), np.array([[10, 11]]), np.array([[20, 21]])],
+         [np.array([[0, 1, 2, 3]]), np.array([[10, 11, 12, 13]]), np.array([[20, 21, 22, 23]])])
+        ]
     assert_data_fitted_properly(actual_fitted_data, expected_fitted_data)
 
 
@@ -384,19 +660,19 @@ def test_column_transformer_transform_should_support_multiple_tuples():
     # Given
     test_case = ColumnChooserTestCase(
         data_inputs=np.array([
-            [1, 1, 2, 3],
-            [10, 11, 12, 13],
-            [20, 21, 22, 23]
+            [[1, 1, 2, 3]],
+            [[10, 11, 12, 13]],
+            [[20, 21, 22, 23]]
         ]),
         expected_outputs=np.array([
-            [0, 1, 2, 3],
-            [10, 11, 12, 13],
-            [20, 21, 22, 23]
+            [[0, 1, 2, 3]],
+            [[10, 11, 12, 13]],
+            [[20, 21, 22, 23]]
         ]),
         expected_processed_outputs=np.array([
-            [2, 2, 4],
-            [20, 22, 24],
-            [40, 42, 44]
+            [[2, 2, 4]],
+            [[20, 22, 24]],
+            [[40, 42, 44]]
         ]),
         column_transformer_tuple_list=[
             (slice(0, 2), MultiplyBy2()),
@@ -405,7 +681,7 @@ def test_column_transformer_transform_should_support_multiple_tuples():
         n_dimension=3
     )
     data_inputs = test_case.data_inputs
-    p = ColumnTransformer(test_case.column_transformer_tuple_list)
+    p = ColumnTransformer(test_case.column_transformer_tuple_list, test_case.n_dimension)
 
     # When
     outputs = p.transform(data_inputs)
diff --git a/testing/test_context_logger.py b/testing/test_context_logger.py
index 37671ce3..990eb9a5 100644
--- a/testing/test_context_logger.py
+++ b/testing/test_context_logger.py
@@ -10,7 +10,7 @@
 from neuraxle.hyperparams.distributions import FixedHyperparameter
 from neuraxle.hyperparams.space import HyperparameterSpace
 from neuraxle.metaopt.auto_ml import AutoML, InMemoryHyperparamsRepository, RandomSearchHyperparameterSelectionStrategy, \
-    ValidationSplitter
+    ValidationSplitter, HyperparamsJSONRepository
 from neuraxle.metaopt.callbacks import ScoringCallback
 from neuraxle.pipeline import Pipeline
 from neuraxle.steps.numpy import MultiplyByN, NumpyReshape
@@ -73,12 +73,11 @@ def test_logger():
     os.remove(file_path)
 
 class TestTrialLogger:
-
-    def test_logger_automl_multiprocessing(self, tmpdir):
+    def test_logger_automl(self, tmpdir):
         # Given
         context = ExecutionContext()
         self.tmpdir = str(tmpdir)
-        hp_repository = InMemoryHyperparamsRepository(cache_folder=self.tmpdir)
+        hp_repository = HyperparamsJSONRepository(cache_folder=self.tmpdir)
         n_epochs = 2
         n_trials = 4
         auto_ml = AutoML(
@@ -113,9 +112,10 @@ def test_logger_automl_multiprocessing(self, tmpdir):
             assert os.path.exists(f)
 
         # That not a great way of testing... but at least it raises a flag when something changes in the logging process
-        with open(file_paths[0], 'r') as f:
-            l = f.readlines()
-            assert len(l) == 36
+        for f in file_paths:
+            with open(f, 'r') as f:
+                l = f.readlines()
+                assert len(l) == 30
 
 
     def teardown(self):
diff --git a/testing/test_pipeline_setup_teardown.py b/testing/test_pipeline_setup_teardown.py
index 08d3e219..534a947a 100644
--- a/testing/test_pipeline_setup_teardown.py
+++ b/testing/test_pipeline_setup_teardown.py
@@ -1,6 +1,5 @@
 from typing import Any
 
-import pytest
 
 from neuraxle.base import NamedTupleList, BaseStep, ExecutionContext
 from neuraxle.pipeline import Pipeline
@@ -44,21 +43,27 @@ def test_fit_transform_should_setup_pipeline_and_steps():
         step_setup
     ])
 
+    assert not p.is_initialized
+    assert not step_setup.is_initialized
+
     p.fit_transform([1], [1])
 
     assert p.is_initialized
+    assert step_setup.is_initialized
 
 
-def test_transform_should_not_setup_pipeline_and_steps():
+def test_transform_should_setup_pipeline_and_steps():
     step_setup = SomeStepSetup()
     p = SomePipeline([
         step_setup
     ])
     assert not p.is_initialized
+    assert not step_setup.is_initialized
 
     p.transform([1])
 
-    assert not p.is_initialized
+    assert p.is_initialized
+    assert step_setup.is_initialized
 
 
 def test_fit_should_setup_pipeline_and_steps():
@@ -66,7 +71,10 @@ def test_fit_should_setup_pipeline_and_steps():
     p = SomePipeline([
         step_setup
     ])
+    assert not p.is_initialized
+    assert not step_setup.is_initialized
 
     p.fit([1], [1])
 
-    assert step_setup.is_initialized
+    assert p.is_initialized
+    assert step_setup.is_initialized
\ No newline at end of file
diff --git a/testing/test_service_assertions.py b/testing/test_service_assertions.py
index bdb51db5..77be4cad 100644
--- a/testing/test_service_assertions.py
+++ b/testing/test_service_assertions.py
@@ -1,4 +1,5 @@
 import os
+import shutil
 from abc import ABC, abstractmethod
 
 import numpy as np
@@ -8,7 +9,7 @@
 from neuraxle.base import Identity, ExecutionContext, ForceHandleMixin, StepWithContext, ForceHandleIdentity
 from neuraxle.data_container import DataContainer
 from neuraxle.metaopt.auto_ml import InMemoryHyperparamsRepository, AutoML, RandomSearchHyperparameterSelectionStrategy, \
-    ValidationSplitter
+    ValidationSplitter, HyperparamsJSONRepository
 from neuraxle.metaopt.callbacks import ScoringCallback
 from neuraxle.pipeline import Pipeline
 
@@ -38,24 +39,6 @@ def _will_process(self, data_container: DataContainer, context: ExecutionContext
         return data_container, context
 
 
-def test_step_with_context_saver(tmpdir):
-    context = ExecutionContext(root=tmpdir)
-    service = SomeService()
-    pipeline_name = 'testname'
-    context.set_service_locator({SomeBaseService: service})
-    p = Pipeline([
-        SomeStep().assert_has_services(SomeBaseService)
-    ]).with_context(context=context)
-    p.set_name(pipeline_name)
-    p.save(context, full_dump=True)
-
-    p: StepWithContext = ExecutionContext(root=tmpdir).load(pipeline_name)
-    assert isinstance(p, StepWithContext)
-
-    p: Pipeline = ExecutionContext(root=tmpdir).load(os.path.join(pipeline_name, 'Pipeline'))
-    assert isinstance(p, Pipeline)
-
-
 def test_with_context_should_inject_dependencies_properly(tmpdir):
     data_inputs = np.array([0, 1, 2, 3])
     context = ExecutionContext(root=tmpdir)
@@ -111,7 +94,8 @@ def test_localassert_should_fail_when_services_are_missing_at_exec(tmpdir):
 
 
 def _make_autoML_loop(tmpdir, p: Pipeline):
-    hp_repository = InMemoryHyperparamsRepository(cache_folder=str(tmpdir) + "_hp")
+    hp_repository = HyperparamsJSONRepository(cache_folder=tmpdir)
+#    hp_repository = InMemoryHyperparamsRepository(cache_folder=str(tmpdir) + "_hp")
     n_epochs = 1
     return AutoML(
         pipeline=p,
@@ -126,76 +110,108 @@ def _make_autoML_loop(tmpdir, p: Pipeline):
         continue_loop_on_error=False
     )
 
+class TestServiceAssertion:
+
+    def _setup(self, tmpdir):
+        self.tmpdir = str(tmpdir)
+        self.tmpdir_hp = self.tmpdir+"_hp"
+
+    def teardown(self):
+        shutil.rmtree(self.tmpdir, ignore_errors=True)
+        shutil.rmtree(self.tmpdir+"_hp", ignore_errors=True)
+
+    def test_step_with_context_saver(self, tmpdir):
+        self._setup(tmpdir)
+        context = ExecutionContext(root=self.tmpdir)
+        service = SomeService()
+        pipeline_name = 'testname'
+        context.set_service_locator({SomeBaseService: service})
+        p = Pipeline([
+            SomeStep().assert_has_services(SomeBaseService)
+        ]).with_context(context=context)
+        p.set_name(pipeline_name)
+        p.save(context, full_dump=True)
+
+        p: StepWithContext = ExecutionContext(root=self.tmpdir).load(pipeline_name)
+        assert isinstance(p, StepWithContext)
+
+        p: Pipeline = ExecutionContext(root=self.tmpdir).load(os.path.join(pipeline_name, 'Pipeline'))
+        assert isinstance(p, Pipeline)
+
+
+    def test_auto_ml_should_inject_dependencies_properly(self,tmpdir):
+        self._setup(tmpdir)
+        data_inputs = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        expected_outputs = data_inputs * 2
+        p = Pipeline([
+            SomeStep().assert_has_services(SomeBaseService),
+            SomeStep().assert_has_services_at_execution(SomeBaseService)
+        ])
+        context = ExecutionContext(root=self.tmpdir)
+        service = SomeService()
+        context.set_service_locator({SomeBaseService: service})
+
+        auto_ml: AutoML = _make_autoML_loop(self.tmpdir_hp, p)
+        auto_ml: StepWithContext = auto_ml.with_context(context=context)
+        assert isinstance(auto_ml, StepWithContext)
+        auto_ml.fit(data_inputs, expected_outputs)
 
-def test_auto_ml_should_inject_dependencies_properly(tmpdir):
-    data_inputs = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
-    expected_outputs = data_inputs * 2
-    p = Pipeline([
-        SomeStep().assert_has_services(SomeBaseService),
-        SomeStep().assert_has_services_at_execution(SomeBaseService)
-    ])
-    context = ExecutionContext(root=tmpdir)
-    service = SomeService()
-    context.set_service_locator({SomeBaseService: service})
+        assert np.array_equal(service.data, data_inputs)
 
-    auto_ml: AutoML = _make_autoML_loop(tmpdir, p)
-    auto_ml: StepWithContext = auto_ml.with_context(context=context)
-    assert isinstance(auto_ml, StepWithContext)
-    auto_ml.fit(data_inputs, expected_outputs)
 
-    assert np.array_equal(service.data, data_inputs)
+    def test_auto_ml_should_fail_at_init_when_services_are_missing(self, tmpdir):
+        self._setup(tmpdir)
+        data_inputs = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        expected_outputs = data_inputs * 2
+        p = Pipeline([
+            RegisterServiceDynamically(),
+            SomeStep().assert_has_services(SomeBaseService),
+        ])
 
+        context = ExecutionContext(root=self.tmpdir)
 
-def test_auto_ml_should_fail_at_init_when_services_are_missing(tmpdir):
-    data_inputs = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
-    expected_outputs = data_inputs * 2
-    p = Pipeline([
-        RegisterServiceDynamically(),
-        SomeStep().assert_has_services(SomeBaseService),
-    ])
+        auto_ml: AutoML = _make_autoML_loop(self.tmpdir_hp, p)
+        auto_ml: StepWithContext = auto_ml.with_context(context=context)
+        assert isinstance(auto_ml, StepWithContext)
 
-    context = ExecutionContext(root=tmpdir)
+        with pytest.raises(AssertionError) as exception_info:
+            auto_ml.fit(data_inputs, expected_outputs)
 
-    auto_ml: AutoML = _make_autoML_loop(tmpdir, p)
-    auto_ml: StepWithContext = auto_ml.with_context(context=context)
-    assert isinstance(auto_ml, StepWithContext)
+        assert 'SomeBaseService dependency missing' in exception_info.value.args[0]
 
-    with pytest.raises(AssertionError) as exception_info:
-        auto_ml.fit(data_inputs, expected_outputs)
 
-    assert 'SomeBaseService dependency missing' in exception_info.value.args[0]
+    def test_auto_ml_should_fail_at_exec_when_services_are_missing(self, tmpdir):
+        self._setup(tmpdir)
+        data_inputs = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        expected_outputs = data_inputs * 2
+        p = Pipeline([
+            SomeStep().assert_has_services_at_execution(SomeBaseService),
+        ])
+        context = ExecutionContext(root=self.tmpdir)
 
+        auto_ml: AutoML = _make_autoML_loop(self.tmpdir_hp, p)
+        auto_ml: StepWithContext = auto_ml.with_context(context=context)
+        assert isinstance(auto_ml, StepWithContext)
 
-def test_auto_ml_should_fail_at_exec_when_services_are_missing(tmpdir):
-    data_inputs = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
-    expected_outputs = data_inputs * 2
-    p = Pipeline([
-        SomeStep().assert_has_services_at_execution(SomeBaseService),
-    ])
-    context = ExecutionContext(root=tmpdir)
+        with pytest.raises(AssertionError) as exception_info:
+            auto_ml.fit(data_inputs, expected_outputs)
+        assert 'SomeBaseService dependency missing' in exception_info.value.args[0]
 
-    auto_ml: AutoML = _make_autoML_loop(tmpdir, p)
-    auto_ml: StepWithContext = auto_ml.with_context(context=context)
-    assert isinstance(auto_ml, StepWithContext)
 
-    with pytest.raises(AssertionError) as exception_info:
-        auto_ml.fit(data_inputs, expected_outputs)
-    assert 'SomeBaseService dependency missing' in exception_info.value.args[0]
+    def test_auto_ml_should_assert_dependecies_properly_at_exec(self, tmpdir):
+        self._setup(tmpdir)
+        data_inputs = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        expected_outputs = data_inputs * 2
+        p = Pipeline([
+            RegisterServiceDynamically(),
+            SomeStep().assert_has_services_at_execution(SomeBaseService),
+        ])
+        context = ExecutionContext(root=self.tmpdir)
 
+        auto_ml: AutoML = _make_autoML_loop(self.tmpdir_hp, p)
+        auto_ml: StepWithContext = auto_ml.with_context(context=context)
+        assert isinstance(auto_ml, StepWithContext)
+        auto_ml.fit(data_inputs, expected_outputs)
 
-def test_auto_ml_should_assert_dependecies_properly_at_exec(tmpdir):
-    data_inputs = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
-    expected_outputs = data_inputs * 2
-    p = Pipeline([
-        RegisterServiceDynamically(),
-        SomeStep().assert_has_services_at_execution(SomeBaseService),
-    ])
-    context = ExecutionContext(root=tmpdir)
-
-    auto_ml: AutoML = _make_autoML_loop(tmpdir, p)
-    auto_ml: StepWithContext = auto_ml.with_context(context=context)
-    assert isinstance(auto_ml, StepWithContext)
-    auto_ml.fit(data_inputs, expected_outputs)
-
-    service = context.get_service(SomeBaseService)
-    assert np.array_equal(service.data, data_inputs)
+        service = context.get_service(SomeBaseService)
+        assert np.array_equal(service.data, data_inputs)