From 7f09034386d336d2effa811b42b45a728bca082f Mon Sep 17 00:00:00 2001
From: aatishpr <aatishpr@buffalo.edu>
Date: Thu, 21 Oct 2021 13:59:06 -0400
Subject: [PATCH 1/4] GUI for GA (MLPRegressor) added

---
 README.md                                     |  28 ++-
 chemml/datasets/GA_files/error_metric.txt     |  11 +
 chemml/datasets/GA_files/ga_eval.txt          |  27 +++
 chemml/datasets/GA_files/single_obj.txt       |  12 +
 chemml/datasets/GA_files/space.txt            |   1 +
 .../GA_files/test_hyperparameters.txt         |   6 +
 chemml/wrapper/base.py                        |   2 +-
 chemml/wrapper/chemml_cml/chemml_wrapper.py   | 226 ++++++++++++++++++
 chemml/wrapper/database/chemml_db.py          |  78 ++++--
 chemml/wrapper/database/containers.py         |   4 +-
 chemml/wrapper/database/sklearn_db.py         |   2 +
 chemml/wrapper/engine.py                      |   3 +-
 chemml/wrapper/notebook/main.py               |  58 ++++-
 chemml/wrapper/notebook/templates.py          |  80 +++++++
 docs/CMLWInputFileGenerator.rst               |  23 +-
 docs/CMLWTutorial.rst                         |   2 +-
 16 files changed, 514 insertions(+), 49 deletions(-)
 create mode 100644 chemml/datasets/GA_files/error_metric.txt
 create mode 100644 chemml/datasets/GA_files/ga_eval.txt
 create mode 100644 chemml/datasets/GA_files/single_obj.txt
 create mode 100644 chemml/datasets/GA_files/space.txt
 create mode 100644 chemml/datasets/GA_files/test_hyperparameters.txt

diff --git a/README.md b/README.md
index 1b6d9151..b4a58871 100644
--- a/README.md
+++ b/README.md
@@ -22,10 +22,7 @@ Please check the [ChemML website](https://hachmannlab.github.io/chemml) for more
 ChemML is developed in the Python 3 programming language and makes use of a host of data analysis and ML libraries(accessible through the Anaconda distribution), as well as domain-specific libraries. 
 The development follows a strictly modular and object-oriented design to make the overall code as flexible and versatile as possible.
 
-The format of library is similar to the well known libraries like Scikit-learn. ChemML will be soon available 
-via graphical user interface provided by [ChemEco](https://github.com/hachmannlab/chemeco).
-ChemEco is a general-purpose framework for data mining without coding. It also interfaces with many of the libraries that supply methods for the 
-representation, preprocessing, analysis, mining, and modeling of large-scale chemical data sets.
+The format of library is similar to the well known libraries like Scikit-learn. 
 
 
 ## Latest Version:
@@ -44,12 +41,15 @@ Here is a list of external libraries that will be installed with chemml:
    - matplotlib
    - seaborn
    - lxml
+   - graphviz
+   - openpyxl
+   - ipywidgets
 
-Since conda installation is not available for ChemML yet, we recommend installing rdkit and openbabel (please install openbabel 2.x not openbabel 3.x) in a conda virtual environment prior to installing ChemML. For doing so, you need to follow the conda installer:
+We strongly recommend you to install ChemML in an Anaconda environment. The instructions to create the environment, install ChemML’s dependencies, and subsequently install Chemml using the Python Package Index (PyPI) via pip are as follows:
 
-    conda create --name my_chemml_env python=3.6
-    source activate my_chemml_env
-    conda install -c conda-forge rdkit openbabel
+    conda create --name chemml_env python=3.8
+    source activate chemml_env
+    conda install -c conda-forge openbabel rdkit nb_conda_kernels python-graphviz
     pip install chemml
 
 ## Citation:
@@ -93,6 +93,13 @@ Please cite the use of ChemML as:
     year = {2018}
     }
 
+    @article{vishwakarma2019towards,
+    title={Towards autonomous machine learning in chemistry via evolutionary algorithms},
+    author={Vishwakarma, Gaurav and Haghighatlari, Mojtaba and Hachmann, Johannes},
+    journal={ChemRxiv preprint},
+    year={2019}
+    }
+
 ## License:
 ChemML is copyright (C) 2014-2018 Johannes Hachmann and Mojtaba Haghighatlari, all rights reserved.
 ChemML is distributed under 3-Clause BSD License (https://opensource.org/licenses/BSD-3-Clause).
@@ -102,7 +109,8 @@ ChemML is distributed under 3-Clause BSD License (https://opensource.org/license
 ### Maintainers:
     - Johannes Hachmann, hachmann@buffalo.edu
     - Mojtaba Haghighatlari
-    - Aditya Sonpal
+    - Aditya Sonpal, adityaso@buffalo.edu
+    - Aatish Pradhan, aatishpr@buffalo.edu
     University at Buffalo - The State University of New York (UB)
 
 ### Contributors:
@@ -110,9 +118,11 @@ ChemML is distributed under 3-Clause BSD License (https://opensource.org/license
     - Gaurav Vishwakarma (UB): automated model optimization
     - Ramachandran Subramanian (UB): Magpie descriptor library port
     - Bhargava Urala Kota (UB): library database
+    - Aditya Sonpal (UB): graph convolution NNs
     - Srirangaraj Setlur (UB): scientific advice
     - Venugopal Govindaraju (UB): scientific advice
     - Krishna Rajan (UB): scientific advice
+    - Aatish Pradhan (UB): Jupyter GUI developer
 
     - We encourage any contributions and feedback. Feel free to fork and make pull-request to the "development" branch.
 
diff --git a/chemml/datasets/GA_files/error_metric.txt b/chemml/datasets/GA_files/error_metric.txt
new file mode 100644
index 00000000..8dfa0b69
--- /dev/null
+++ b/chemml/datasets/GA_files/error_metric.txt
@@ -0,0 +1,11 @@
+def error_metric(y_true,y_pred):
+    y_true = np.asarray(y_true)
+    y_pred = np.asarray(y_pred)
+    ndata = len(y_true)
+    y_mean = np.mean(y_true)
+    e = y_true - y_pred
+    ae = np.absolute(e)
+    se = np.square(e)
+    var = np.mean(np.square(y_true - y_mean))
+    MAE = np.mean(ae)
+    return MAE
\ No newline at end of file
diff --git a/chemml/datasets/GA_files/ga_eval.txt b/chemml/datasets/GA_files/ga_eval.txt
new file mode 100644
index 00000000..c622a3d0
--- /dev/null
+++ b/chemml/datasets/GA_files/ga_eval.txt
@@ -0,0 +1,27 @@
+def ga_eval(indi):
+        
+        layers = [indi[i] for i in range(2,5) if indi[i] != 0]
+        #print(np.exp(indi[0]))
+        
+        #count iterations of GA
+        count=open("tmp.txt", "a")
+        count.write("GA search iteration in process... \n")
+        count.close()
+        file = open("tmp.txt","r")
+        Counter = 0
+        # Reading number of lines from file
+        Content = file.read()
+        CoList = Content.split("\n")
+        for i in CoList:
+            if i:
+                Counter += 1
+        print("GA search iteration in process... ",Counter)
+        mlp = MLPRegressor(alpha=np.exp(indi[0]), activation=indi[1], hidden_layer_sizes=tuple(layers),learning_rate='invscaling', max_iter=10,early_stopping=True)
+        ga_search = single_obj(mlp=mlp, x=X.values, y=Y.values,n_splits=n_splits)
+        #print("GA search iteration in process...")
+        f=open("GA.txt", "a")
+        f.write("%f %s %d %d %d %f \n" %(float(np.exp(indi[0])), str(indi[1]), int(indi[2]), int(indi[3]), int(indi[4]),float(ga_search)))
+        f.close()
+        #gui_return ={"ga_search": ga_search}
+        #print(gui_return)
+        return ga_search
\ No newline at end of file
diff --git a/chemml/datasets/GA_files/single_obj.txt b/chemml/datasets/GA_files/single_obj.txt
new file mode 100644
index 00000000..bbb22f96
--- /dev/null
+++ b/chemml/datasets/GA_files/single_obj.txt
@@ -0,0 +1,12 @@
+def single_obj(mlp, x, y, n_splits=n_splits):
+    n_splits=n_splits
+    kf = KFold(n_splits)                                                      # cross validation based on Kfold (creates 5 validation train-test sets)
+    accuracy_kfold = []
+    for training, testing in kf.split(x):
+        mlp.fit(x[training], y[training])
+        y_pred = mlp.predict(x[testing])
+        y_pred, y_act =y_pred.reshape(-1,1), y[testing].reshape(-1,1)
+        model_accuracy=mae(y_act,y_pred)                             # evaluation metric:  mae
+        accuracy_kfold.append(model_accuracy)                                   # creates list of accuracies for each fold
+    #print("def single_obj - completed")
+    return np.mean(accuracy_kfold)
\ No newline at end of file
diff --git a/chemml/datasets/GA_files/space.txt b/chemml/datasets/GA_files/space.txt
new file mode 100644
index 00000000..bb35df70
--- /dev/null
+++ b/chemml/datasets/GA_files/space.txt
@@ -0,0 +1 @@
+space = ({'alpha': {'uniform': [np.log(0.0001), np.log(0.1)], 'mutation': [0, 1]}},{'activation': {'choice': ['identity', 'logistic', 'tanh', 'relu']}},{'neurons1':  {'choice': range(0,220,20)}},{'neurons2':  {'choice': range(0,220,20)}},{'neurons3':  {'choice': range(0,220,20)}})
\ No newline at end of file
diff --git a/chemml/datasets/GA_files/test_hyperparameters.txt b/chemml/datasets/GA_files/test_hyperparameters.txt
new file mode 100644
index 00000000..51949996
--- /dev/null
+++ b/chemml/datasets/GA_files/test_hyperparameters.txt
@@ -0,0 +1,6 @@
+def test_hyp(mlp, x, y, xtest, ytest):                                          
+    mlp.fit(x, y)
+    ypred = mlp.predict(xtest)
+    acc=mae(ytest,ypred)
+    # print(" test_hyp completed ")
+    return np.mean(acc)
\ No newline at end of file
diff --git a/chemml/wrapper/base.py b/chemml/wrapper/base.py
index e9f3cb5c..0d851104 100644
--- a/chemml/wrapper/base.py
+++ b/chemml/wrapper/base.py
@@ -339,7 +339,7 @@ def references(self,host,function):
                 # ref_p = "@misc{chollet2015keras,title={Keras},author={Chollet, Fran\c{c}ois and others},year={2015},publisher={GitHub},howpublished={\url{https://github.com/keras-team/keras}},}"
                 ref_p = "ABCD"
                 self.refs['keras'] = {'url': ref_g, 'paper': ref_p}
-            elif function in ['GA_DEAP']:
+            elif function in ['GA']:
                 ref_g = "https://github.com/deap/deap"
                 ref_p = """@article{DEAP_JMLR2012,
                                 author    = " F\'elix-Antoine Fortin and Fran\c{c}ois-Michel {De Rainville} and Marc-Andr\'e Gardner and Marc Parizeau and Christian Gagn\'e ",
diff --git a/chemml/wrapper/chemml_cml/chemml_wrapper.py b/chemml/wrapper/chemml_cml/chemml_wrapper.py
index 863847c2..24558111 100644
--- a/chemml/wrapper/chemml_cml/chemml_wrapper.py
+++ b/chemml/wrapper/chemml_cml/chemml_wrapper.py
@@ -1868,3 +1868,229 @@ def fit(self):
         # step7: delete all inputs from memory
         del self.inputs
 
+class GA(BASE):
+    def fit(self):
+        self.paramFROMinput()
+        # txt_files = list(self.parameters.keys())[:4]      #keys = evaluate, space, error_metric, objecive
+        # ga_eval = self.parameters[txt_files[0]]
+        # space = self.parameters[txt_files[1]]
+        # error_metric = self.parameters[txt_files[2]]
+        # single_obj = self.parameters[txt_files[3]]
+        
+        # for key in self.parameters:
+        #     print(key," : ", self.parameters[key])
+        
+            
+        try:
+            from chemml.optimization import GeneticAlgorithm
+            from sklearn.neural_network import MLPRegressor
+            from sklearn.model_selection import train_test_split
+            from datetime import date, datetime
+
+
+            import numpy as np
+            
+            # for files in list(self.parameters.keys())[:4]:      #keys = evaluate, space, error_metric, objecive
+            #     with open(self.parameters[files],'r') as f:
+            #         contents = f.read()
+            #         # print("files: ", files, "contents: ", contents)
+            #     files = compile(contents, "<string>", "exec")
+            
+            for key in list(self.parameters.keys()):
+                if key == 'fitness':
+                    final_fit=[]
+                    fitness = str(self.parameters[key])[-6:-3]
+                    fitness = fitness[0]+fitness[1]+fitness[2]
+                    final_fit.append(fitness)
+                    final_fit = tuple(final_fit)
+                    # print("fitness: ",final_fit)
+                    # print("type(fitness): ", type(final_fit))
+                elif key == 'pop_size':
+                    pop_size = self.parameters[key]
+                elif key == 'crossover_size':
+                    crossover_size = self.parameters[key]
+                elif key == 'mutation_size':
+                    mutation_size = self.parameters[key]
+                elif key == 'n_splits':
+                    global n_splits
+                    n_splits = self.parameters[key]
+                elif key == 'crossover_type':
+                    crossover_type = self.parameters[key]
+                elif key == 'mutation_prob':
+                    mutation_prob = self.parameters[key]
+                elif key == 'initial_population':
+                    initial_population = self.parameters[key]
+                elif key == 'n_generations':
+                    n_generations = self.parameters[key]
+                elif key == 'early_stopping':
+                    early_stopping = self.parameters[key]
+                elif key == 'init_ratio':
+                   init_ratio = self.parameters[key]
+                elif key == 'crossover_ratio':
+                    crossover_ratio = self.parameters[key]
+                elif key == 'algorithm':
+                    global algorithm
+                    algorithm = self.parameters[key]
+
+            #default in chemml.optimizaiton.geneticalgorithm
+            if 'early_stopping' not in list(self.parameters.keys())[4:]:
+                early_stopping = 10
+
+            with open(self.parameters['error_metric'],'r') as f:
+                contents = f.read()
+                    # print("files: ", files, "contents: ", contents)
+                code = compile(contents, "<string>", "exec")
+                loc = {}
+                try:
+                    exec(code,globals(), loc)
+                    global mae
+                    mae = loc['error_metric']
+                except:
+                    print("Something wrong with the code...")
+                    print("error_metric: ", mae)
+                    print("type(error_metric): ",type(mae))
+
+            with open(self.parameters['space'],'r') as f:
+                contents = f.read()
+                code = compile(contents, "<string>", "exec")
+                loc = {}
+                try:
+                    exec(code,globals(), loc)
+                    space = loc['space']
+                except:
+                    print("Something wrong with the code...")
+                    print("Space: ", space)
+                    print("type(space): ",type(space))
+
+                            
+            with open(self.parameters['single_obj'],'r') as f:
+                contents = f.read()
+                    # print("files: ", files, "contents: ", contents)
+                code = compile(contents, "<string>", "exec")
+                loc = {}
+                try:
+                    exec(code,globals(), loc)
+                    global single_obj
+                    single_obj = loc['single_obj']
+                except:
+                    print("Something wrong with the code...")
+                    print("single_obj: ", single_obj)
+                    print("type(single_obj): ",type(single_obj))
+                
+                # print("single_obj: ", single_obj)
+                # print("type(single_obj): ",type(single_obj))
+
+            with open(self.parameters['evaluate'],'r') as f:
+                contents = f.read()
+                    # print("files: ", files, "contents: ", contents)
+                code = compile(contents, "<string>", "exec")
+                loc = {}
+                try:
+                    exec(code,globals(), loc)
+                    ga_eval = loc['ga_eval']
+                except:
+                    print("Something wrong with the code...")
+                    print("ga_eval: ", ga_eval)
+                    print("type(ga_eval): ",type(ga_eval))
+                # print("ga_eval: ", ga_eval)
+                # print("type(ga_eval): ",type(ga_eval))
+            
+            with open(self.parameters['test_hyperparameters'],'r') as f:
+                contents = f.read()
+                    # print("files: ", files, "contents: ", contents)
+                code = compile(contents, "<string>", "exec")
+                loc = {}
+                try:
+                    exec(code,globals(), loc)
+                    test_hyp = loc['test_hyp']
+                except:
+                    print("Something wrong with the code...")
+                    print("test_hyperparameters: ", test_hyperparameters)
+                    print("type(test_hyperparameters): ",type(test_hyperparameters))
+
+
+            ##### GA happening here#########
+            def ga_mlpregressor(x_train, y_train, x_test, y_test, al=algorithm,n_splits=n_splits,n_generations=n_generations,early_stopping=early_stopping): 
+                global X 
+                global Y
+                X=x_train
+                Y=y_train 
+                print("Hyperparameter optimization is a time consuming process - do not shutdown Kernel....\n")
+                print('Total GA search iterations = ', n_generations*pop_size)
+                gann = GeneticAlgorithm(evaluate=ga_eval, space=space, fitness=final_fit, pop_size = pop_size, crossover_size=crossover_size, mutation_size=mutation_size, algorithm=al)
+                global MLPRegressor
+                from sklearn.neural_network import MLPRegressor
+                global KFold
+                from sklearn.model_selection import KFold
+                import warnings
+                warnings.filterwarnings("ignore")  
+                best_ind_df, best_individual = gann.search(n_generations=n_generations, early_stopping=early_stopping)                     # set pop_size<30, n_generations*pop_size = no. of times GA runs                      
+                print("GeneticAlgorithm - complete!")
+ 
+                all_items = list(gann.fitness_dict.items())
+                all_items_df = pd.DataFrame(all_items, columns=['hyperparameters', 'Accuracy_score'])
+                print("\n\ngenetic algorithm results for each generation: \n", best_ind_df, "\n\nbest particle: ", best_individual, "\n")
+                print("Calculating accuracy on test data....")
+                l = [best_individual['neurons1'], best_individual['neurons2'], best_individual['neurons3']]
+                layers = [i for i in l if i != 0]
+                ga_mlp = MLPRegressor(alpha=np.exp(best_individual['alpha']), activation=best_individual['activation'], hidden_layer_sizes=tuple(layers), learning_rate='invscaling', max_iter=20, early_stopping=True)
+                ga_accuracy_test = test_hyp(mlp=ga_mlp, x=X, y=Y, xtest=x_test, ytest=y_test)
+                print("\n\nTest set error_metric (default = MAE) for the best GA hyperparameter: ", ga_accuracy_test, "\n")
+                return all_items_df , best_ind_df
+
+
+            #Read data here
+            self.required('dfx_train', req=True)
+            dfx_train= self.inputs['dfx_train'].value
+            self.required('dfy_train', req=True)
+            dfy_train= self.inputs['dfy_train'].value
+            self.required('dfx_test', req=True)
+            dfx_test= self.inputs['dfx_test'].value
+            self.required('dfy_test', req=True)
+            dfy_test= self.inputs['dfy_test'].value
+
+
+            # dfx_train = self.inputs['dfx_train'].value
+            # dfy_train = self.inputs['dfy_train'].value
+            # dfx_test = self.inputs['dfx_test'].value
+            # dfy_test = self.inputs['dfy_test'].value
+        
+            # type of ML model defined here
+            for key in list(self.parameters.keys()):
+                if key == 'ml_model':
+                    ml_model = self.parameters[key]
+                    if ml_model == 'MLPRegressor':
+                        
+                        best_ind_df, best_individual = ga_mlpregressor(x_train=dfx_train, y_train=dfy_train, x_test=dfx_test,y_test=dfy_test, al = algorithm, n_splits=n_splits, n_generations=n_generations, early_stopping=early_stopping)
+                        # print(all_items_df)
+
+
+            os.remove("tmp.txt")            #remove tmp file to count  umber of GA iterations
+            os.remove("GA.txt")             #remove file with all GA iterations
+
+            # now = datetime.now()            #to save with current date and time
+            # dt_string = now.strftime("%m-%d-%Y %H-%M-%S")
+            # all_items_df.to_csv('best_ind_df' + str(dt_string) + '.csv')
+            # best_ind_df.to_csv('best_individual' + str(dt_string) + '.csv')
+            # print("GA DONEE!!!")
+
+        except Exception as err:
+            msg = '@Task #%i(%s): '%(self.iblock+1, self.Task) + type(err).__name__ + ': '+ str(err)
+            raise TypeError(msg)
+
+        order = [edge[1] for edge in self.Base.graph if edge[0] == self.iblock]
+        for token in set(order):
+            if token not in self.outputs:
+                msg = "@Task #%i(%s): not a valid output token '%s'" % (self.iblock + 1, self.Task, token)
+                raise NameError(msg)
+            elif token == 'best_ind_df':
+                self.set_value(token, best_ind_df)
+                self.outputs[token].count = order.count(token)
+                self.Base.send[(self.iblock, token)] = self.outputs[token]
+            elif token == 'best_individual':
+                self.set_value(token, best_individual)
+                self.outputs[token].count = order.count(token)
+                self.Base.send[(self.iblock, token)] = self.outputs[token]
+
+        # step7: delete all inputs from memory
+        del self.inputs
\ No newline at end of file
diff --git a/chemml/wrapper/database/chemml_db.py b/chemml/wrapper/database/chemml_db.py
index 1fc9ba79..0e94890f 100644
--- a/chemml/wrapper/database/chemml_db.py
+++ b/chemml/wrapper/database/chemml_db.py
@@ -554,46 +554,74 @@ class FParameters:
         format=Parameter('format','png')
         kwargs = Parameter('kwargs', {})
 
-class GA_DEAP(object):
+class GA(object):
     task='Optimize'
     subtask = 'genetic algorithm'
     host = 'chemml'
-    function = 'GA_DEAP'
+    function = 'GA'
     modules = ('chemml','optimize')
     requirements = (req(0),req(2),req(10))
-    documentation = ""
+    documentation = """Hyperparamter optimization is a time consuming process. The amount of time required to find the best hyperparameters relies on multiple factors which depend on the parameters specified below. Please be patient! 
+    Link: https://github.com/hachmannlab/chemml/blob/master/chemml/optimization/genetic_algorithm.py """
 
     class Inputs:
-        evaluate = Input('evaluate', "a function that receives a list of individuals and returns the score",
-                    ("<type 'function'>",))
+        # evaluate = Input('evaluate', "a function that receives a list of individuals and returns the score",
+                    # ("<type 'function'>",))
+        dfx_test = Input("dfx_test","pandas dataframe", ("<class 'pandas.core.frame.DataFrame'>",))
+        dfy_train = Input("dfy_train","pandas dataframe", ("<class 'pandas.core.frame.DataFrame'>",))
+        dfy_test = Input("dfy_test","pandas dataframe", ("<class 'pandas.core.frame.DataFrame'>",))
+        dfx_train = Input("dfx_train","pandas dataframe", ("<class 'pandas.core.frame.DataFrame'>",))
 
     class Outputs:
         best_ind_df = Output('best_ind_df',"pandas dataframe of best individuals after each iteration", ("<class 'pandas.core.frame.DataFrame'>",))
         best_individual = Output('best_individual', "pandas dataframe of the best individual",("<class 'pandas.core.frame.DataFrame'>",))
 
     class WParameters:
-        func_method = Parameter('func_method','algorithm_1','string',
-                        description = "a method of the GA_DEAP class that should be applied",
-                        options = ('algorithm_1', 'algorithm_2', 'algorithm_3', 'algorithm_4'))
+        algorithm = Parameter('algorithm','1','int',
+                        description = "A method of the GA class that should be applied",
+                        options = (1, 2, 3, 4), required=True)
+        ml_model = Parameter('ml_model','MLPRegressor','string',
+                        description = "ML model for which hyperparameters need to be optmized",
+                        options = ('MLPRegressor', 'More options to be added'),required=True)
 
     class FParameters:
-        Evaluate = Parameter('Evaluate', '@evaluate', required=True)
-        Weights = Parameter('Weights', (-1.0,))
-        chromosome_length = Parameter('chromosome_length', 1)
-        chromosome_type = Parameter('chromosome_type', (1,))
-        bit_limits = Parameter('bit_limits', ((0, 10),))
-        pop_size = Parameter('pop_size', 50)
-        crossover_prob = Parameter('crossover_prob', 0.4)
-        crossover_type = Parameter('crossover_type', 'Blend')
-        mutation_prob = Parameter('mutation_prob', 0.4)
-        mut_float_mean = Parameter('mut_float_mean', 0)
-        mut_float_dev = Parameter('mut_float_dev', 1)
-        mut_int_lower = Parameter('mut_int_lower', (1,))
-        mut_int_upper = Parameter('mut_int_upper', (10,))
-        n_generations = Parameter('n_generations', 20)
-
-        init_pop_frac = Parameter('init_pop_frac', 0.35,'float, only for algorithm 2')
-        crossover_pop_frac = Parameter('crossover_pop_frac', 0.35, 'float, only for algorithms 2 and 4')
+
+        #Note: do not change first 4!!!
+        evaluate = Parameter('evaluate', 'ga_eval.txt', required=True)
+        space = Parameter('space', 'space.txt', required=True)
+        error_metric = Parameter('error_metric', 'error_metric.txt', required=True)
+        test_hyperparameters = Parameter('test_hyperparameters', 'test_hyperparameters.txt', required=True)
+        single_obj = Parameter('single_obj', 'single_obj.txt', required=True)
+        fitness = Parameter('fitness','(min,)', 'tuple', required=True)
+        pop_size = Parameter('pop_size', 5, 'int', required = True)
+        crossover_size = Parameter('crossover_size', 30, 'int', required = True)
+        mutation_size = Parameter('mutation_size', 20, 'int', required = True)
+        n_splits = Parameter('n_splits',5,'int', required = True)
+        crossover_type = Parameter('crossover_type', 'Blend', 'string')
+        mutation_prob = Parameter('mutation_prob', 0.4, 'float')
+        initial_population = Parameter('initial_population', None, 'list')
+        n_generations = Parameter('n_generations', 5, 'int',required = True)
+        early_stopping = Parameter('early_stopping',10, 'int')
+        init_ratio = Parameter('init_ratio',0.4,'float')
+        crossover_ratio = Parameter('crossover_ratio',0.3,'float')
+        # algorithm = Parameter('algorithm','1','int',
+        #                 description = "A method of the GA class that should be applied",
+        #                 options = (1, 2, 3, 4), required=True)
+        # n_splits = Parameter('n_splits',5,'int')
+        # Weights = Parameter('Weights', (-1.0,))
+        # chromosome_length = Parameter('chromosome_length', 1)
+        # chromosome_type = Parameter('chromosome_type', (1,))
+        # bit_limits = Parameter('bit_limits', ((0, 10),))
+        # crossover_prob = Parameter('crossover_prob', 0.4)
+        # mutation_prob = Parameter('mutation_prob', 0.4)
+        # mut_float_mean = Parameter('mut_float_mean', 0)
+        # mut_float_dev = Parameter('mut_float_dev', 1)
+        # mut_int_lower = Parameter('mut_int_lower', (1,))
+        # mut_int_upper = Parameter('mut_int_upper', (10,))
+        
+
+        # init_pop_frac = Parameter('init_pop_frac', 0.35,'float, only for algorithm 2')
+        # crossover_pop_frac = Parameter('crossover_pop_frac', 0.35, 'float, only for algorithms 2 and 4')
 
 
 ####################################################
diff --git a/chemml/wrapper/database/containers.py b/chemml/wrapper/database/containers.py
index 956d8c0d..e1991043 100644
--- a/chemml/wrapper/database/containers.py
+++ b/chemml/wrapper/database/containers.py
@@ -30,7 +30,7 @@ def __init__(self,name,default,format='',required=False,description="",options=[
         self.options = options
 
 def req(ind):
-    all_req = { 0 : ('ChemML','0.6'),
+    all_req = { 0 : ('ChemML','0.8'),
                 1 : ('scikit-learn','0.19.0'),
                 2 : ('pandas', '1.1.3'),
                 3 : ('RDKit','2016.03.1'),
@@ -40,7 +40,7 @@ def req(ind):
                 7 : ('matplotlib','1.5.1'),
                 8 : ('keras', '2.1.2'),
                 9 : ('tensorflow', '1.4.1'),
-                10: ('deap', '1.2.2')
+                10: ('geneticalgorithm', '2.0')
               }
     return all_req[ind]
 
diff --git a/chemml/wrapper/database/sklearn_db.py b/chemml/wrapper/database/sklearn_db.py
index 67a51219..3be566ac 100644
--- a/chemml/wrapper/database/sklearn_db.py
+++ b/chemml/wrapper/database/sklearn_db.py
@@ -1,4 +1,6 @@
 from chemml.wrapper.database.containers import Input, Output, Parameter, req, regression_types, cv_classes
+import warnings
+warnings.filterwarnings("ignore")
 
 class Binarizer(object):
     task = 'Prepare'
diff --git a/chemml/wrapper/engine.py b/chemml/wrapper/engine.py
index cc51ae0a..893378a2 100644
--- a/chemml/wrapper/engine.py
+++ b/chemml/wrapper/engine.py
@@ -323,8 +323,9 @@ def call(self):
             elif host == 'chemml':
                 # check methods
                 legal_functions = [klass[0] for klass in inspect.getmembers(cmlw)]
+                # print("legal_functions: ", legal_functions)
                 if function not in legal_functions:
-                    msg = "@function #%i: couldn't find function '%s' in the module '%s' wrarpper" %(iblock,function,host)
+                    msg = "@function #%i: couldn't find function '%s' in the module '%s' wrapper" %(iblock,function,host)
                     raise NameError(msg)
                 self.references(host,function) # check references
                 self.Base.graph_info[iblock] = (host, function)
diff --git a/chemml/wrapper/notebook/main.py b/chemml/wrapper/notebook/main.py
index a1112b03..569525b4 100644
--- a/chemml/wrapper/notebook/main.py
+++ b/chemml/wrapper/notebook/main.py
@@ -814,6 +814,60 @@ def on_selectTe12_clicked(b):
                                                                # margin='0px 0px 0px 10px'))
         TEMPLATES.append(hboxTe12)
 
+        ######################################################
+
+        ### Hyperparamter Optimization (GA_optimize)
+        headerGA_optimize = widgets.HTML(value='<b> Hyperparamter Optimization: </b>', layout=widgets.Layout(width='50%',margin='10px 0px 10px 0px'))
+        TEMPLATES.append(headerGA_optimize)
+
+
+        #Template15
+        def on_selectTe15_clicked(b):
+            # template15.txt is a chemml wrapper config file
+            from .templates import template15
+            script = template15()
+            old = [i for i in self.pages]
+
+            try:
+                self.parser(script)
+                # update the current_bid
+                self.block_id = max(self.pages)
+                selectTe15.icon = 'check'
+            except Exception as err:
+                print( "Invalid configuration file ...")
+                print( "    IOError: %s"%err.message)
+                print( "... Not loaded!")
+                selectTe15.icon = 'remove'
+                rm = [i for i in self.pages if i not in old]
+                for ib in rm:
+                    if ib in self.pages:
+                        del self.pages[ib]
+
+            self.debut = False
+            self.add_page()
+
+            ## clear ouput and update the graph viz
+            self.graph.close()
+            dot = Digraph(format='png')
+            for edge in self.comp_graph:
+                dot.node('%i' % edge[0], label='%i %s' % (edge[0], self.pages[edge[0]].title))
+                dot.node('%i' % edge[2], label='%i %s' % (edge[2], self.pages[edge[2]].title))
+                dot.edge('%i' % edge[0], '%i' % edge[2], label='%s > %s' % (edge[1], edge[3]), labelfontcolor='green')
+            self.graph = widgets.Image(value=dot.pipe(), format='png')
+            display(self.graph)
+
+        te15 = widgets.Label(value="Template 1: Genetic Algorithm for MLPRegressor")#, layout=widgets.Layout(width='70%'))
+        selectTe15 = widgets.Button(description="Select")
+        selectTe15.style.button_color = 'lightblue'
+        selectTe15.on_click(on_selectTe15_clicked)
+        # viewT1 = widgets.Button(description="Overview")
+        # viewT1.style.button_color = 'lightblue'
+        # viewT1.on_click(on_viewT1_clicked)
+        hboxTe15 = widgets.HBox([te15, selectTe15],layout=widgets.Layout( border='dotted black 1px',justify_content = 'space-between'))
+                                                               # height='40px', align_items='center',   justify_content = 'space-between',
+                                                               # margin='0px 0px 0px 10px'))
+        TEMPLATES.append(hboxTe15)
+
         ######################################################
         # ## TemplateMHL
         # def on_selectTeMHL_clicked(b):
@@ -1645,7 +1699,7 @@ def parser(self, script):
                 continue
         self.loading_bids = range(min(blocks),max(blocks)+1)
         self._options(blocks)
-        # to make self.comp_graph with cmls (onlly requires send and receive)
+        # to make self.comp_graph with cmls (only requires send and receive)
         self._transform()
 
     def _db_extract_function(self, host, function):
@@ -1730,6 +1784,8 @@ def _options(self, blocks):
             self.pages[bid].widget = custom_function_VBox
 
             # change the widget values to the values from config file
+            # print("parameters: ",parameters)
+            # print(self.pages[bid].block_params['fparams'])
             for param in parameters:
                 if param not in ['host', 'function']:
                     if param in self.pages[bid].block_params['wparams']:
diff --git a/chemml/wrapper/notebook/templates.py b/chemml/wrapper/notebook/templates.py
index be928061..aadac997 100644
--- a/chemml/wrapper/notebook/templates.py
+++ b/chemml/wrapper/notebook/templates.py
@@ -718,4 +718,84 @@ def template14():
                     >> fig 13
              """
 
+    return script.strip().split('\n')
+
+def template15():
+    """Genetic Algorithm"""
+    script= """
+                ## (Input,datasets)
+                    << host = chemml
+                    << function = load_cep_homo
+                    >> smiles 0
+                    >> homo 4
+
+                ## (Output,file)
+                    << host = chemml
+                    << function = SaveFile
+                    << format = smi
+                    << header = False
+                    << filename = smiles
+                    >> 0 df
+                    >> filepath 1
+
+                ## (Represent,molecular descriptors)
+                    << host = chemml
+                    << function = RDKitFingerprint
+                    >> 1 molfile
+                    >> df 2
+                    >> df 3
+
+                ## (Output,file)
+                    << host = chemml
+                    << function = SaveFile
+                    << filename = fps_rdkfp
+                    >> 2 df
+
+                ## (Prepare,split)
+                    << host = sklearn
+                    << function = train_test_split
+                    >> 3 dfx
+                    >> 4 dfy
+                    >> dfx_train 5
+                    >> dfy_train 6
+                    >> dfx_test 7
+                    >> dfy_test 8
+
+                ## (Optimize,genetic algorithm)
+                    << host = chemml
+                    << function = GA
+                    << algorithm = 3
+                    << ml_model = MLPRegressor
+                    << evaluate = ./chemml/chemml/datasets/GA_files/ga_eval.txt
+                    << space = ./chemml/chemml/datasets/GA_files/space.txt
+                    << error_metric = ./chemml/chemml/datasets/GA_files/error_metric.txt
+                    << test_hyperparameters = ./chemml/chemml/datasets/GA_files/test_hyperparameters.txt
+                    << single_obj = ./chemml/chemml/datasets/GA_files/single_obj.txt
+                    << fitness = (min,)
+                    << pop_size = 5
+                    << crossover_size = 2
+                    << mutation_size = 2
+                    << n_splits = 5
+                    << n_generations = 5
+                    >> 5 dfx_train
+                    >> 6 dfy_train
+                    >> 7 dfx_test
+                    >> 8 dfy_test
+                    >> best_ind_df 9
+                    >> best_individual 10
+
+                ## (Output,file)
+                    << host = chemml
+                    << function = SaveFile
+                    << format = csv
+                    << filename = best_ind_df
+                    >> 9 df
+
+                ## (Output,file)
+                    << host = chemml
+                    << function = SaveFile
+                    << format = csv
+                    << filename = best_individual
+                    >> 10 df
+            """
     return script.strip().split('\n')
\ No newline at end of file
diff --git a/docs/CMLWInputFileGenerator.rst b/docs/CMLWInputFileGenerator.rst
index f11e9f4b..b756ce0d 100644
--- a/docs/CMLWInputFileGenerator.rst
+++ b/docs/CMLWInputFileGenerator.rst
@@ -4,15 +4,19 @@
 Input File GUI
 ==============
 
+MAC OS and Linux users may run the commands directly in the terminal once ChemML has been installed.
+
+For Windows users, we recommend installing WSL: https://docs.microsoft.com/en-us/windows/wsl/install-win10 
+       * Through the WSL terminal, install ChemML using the Python Package Index (PyPI) via pip as specified in the installation tutorial.
+       * The steps that follow must be run in the WSL terminal.
+
 The ChemML wrapper's graphical user interface (GUI) facilitates the generation of input files.
 
-To install the dependencies for ChemML Wrapper GUI, activate the virtual environment and run the following commands:
+To run a notebook, the user must run the following commands in the terminal with the environment, which has ChemML installed, activated:
 
 .. code:: bash
 
-   conda install nb_conda_kernels
-   pip install graphviz
-   pip install openpyxl
+   jupyter notebook
 
 Requirements:
     - Jupyter notebook
@@ -25,15 +29,16 @@ Requirements:
         * Using graphviz library, you will see a graphical visualization of your project's workflow simultaneously.
         * graphviz will be installed accompanied by chemml via pip.
 
-To run a notebook, you just need to run the following command in the Terminal:
+A Jupyter notebook will open for the current environment.
+In the new Jupyter notebook, ensure the Kernel for the desired environment is activated. This can be done using: 
+        * Under Kernel > Change Kernel > Python (environment name)
 
-.. code:: bash
-
-   jupyter notebook
 
 Run the GUI locally in the Jupyter notebook via: 
 
 .. code:: python
 
     from chemml.wrapper.notebook import ChemMLNotebook
-    ui = ChemMLNotebook()
\ No newline at end of file
+    ui = ChemMLNotebook()
+
+Instructions to run the input files, created using the Jupyter GUI, will appear once the input file is saved using the GUI.
\ No newline at end of file
diff --git a/docs/CMLWTutorial.rst b/docs/CMLWTutorial.rst
index cf20962b..a645f2bc 100644
--- a/docs/CMLWTutorial.rst
+++ b/docs/CMLWTutorial.rst
@@ -2,7 +2,7 @@
 ChemML Wrapper Tutorial
 =======================
 
-...::: ChemML Wrapper is currently available in the version 0.7. :::...
+ChemML Wrapper is currently available in the version 0.8. 
 
 ChemML Wrapper carry out a workflow of operations hosted by different
 open-source or commercial libraries/software.

From 151fa08a27b0f9736d3ca338522835b3b7730b09 Mon Sep 17 00:00:00 2001
From: aatishpr <aatishpr@buffalo.edu>
Date: Sat, 23 Oct 2021 11:32:03 -0400
Subject: [PATCH 2/4] ipython notebook added, index.rst, setup and README.md
 updated

---
 README.md                                |      1 -
 chemml/wrapper/notebook/main.py          |      2 +-
 docs/CMLWTutorial.rst                    |      2 -
 docs/index.rst                           |      5 +-
 docs/ipython_notebooks/GA_tutorial.ipynb | 109347 ++++++++++++++++++++
 setup.py                                 |      2 +-
 6 files changed, 109352 insertions(+), 7 deletions(-)
 create mode 100644 docs/ipython_notebooks/GA_tutorial.ipynb

diff --git a/README.md b/README.md
index b4a58871..3c4228d2 100644
--- a/README.md
+++ b/README.md
@@ -41,7 +41,6 @@ Here is a list of external libraries that will be installed with chemml:
    - matplotlib
    - seaborn
    - lxml
-   - graphviz
    - openpyxl
    - ipywidgets
 
diff --git a/chemml/wrapper/notebook/main.py b/chemml/wrapper/notebook/main.py
index 569525b4..62ed21ef 100644
--- a/chemml/wrapper/notebook/main.py
+++ b/chemml/wrapper/notebook/main.py
@@ -985,7 +985,7 @@ def on_save_clicked(b):
                         for line in lines:
                             config.write(line)
                     print( "\nThe ChemML Wrapper's config file has been successfully saved ...")
-                    print( "    config file path: %s" % path)
+                    print( "    config file name: %s" % path)
                     print( "    current directory: %s" % os.getcwd())
                     print( "    what's next? run the ChemML Wrapper using the config file with the following codes:")
                     print( "        >>> from chemml.wrapper.engine import run")
diff --git a/docs/CMLWTutorial.rst b/docs/CMLWTutorial.rst
index a645f2bc..fb8fd780 100644
--- a/docs/CMLWTutorial.rst
+++ b/docs/CMLWTutorial.rst
@@ -2,8 +2,6 @@
 ChemML Wrapper Tutorial
 =======================
 
-ChemML Wrapper is currently available in the version 0.8. 
-
 ChemML Wrapper carry out a workflow of operations hosted by different
 open-source or commercial libraries/software.
 The workflow is similar to a directed graph and it can be designed in a text file.
diff --git a/docs/index.rst b/docs/index.rst
index 18d8947a..3943cb1f 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -29,8 +29,8 @@ We strongly recommend you to install ChemML in an Anaconda environment. The inst
 .. code:: bash
 
     conda create --name chemml_env python=3.8
-    source activate chemml_env
-    conda install -c conda-forge openbabel rdkit nb_conda_kernels
+    source activate chemml_env    
+    conda install -c conda-forge openbabel rdkit nb_conda_kernels python-graphviz
     pip install chemml
 
 Note: Here is a list of external libraries that will be installed with chemml:
@@ -120,6 +120,7 @@ Please cite the use of ChemML as:
    ./ipython_notebooks/read_excel.ipynb 
    _./ipython_notebooks/pyscript_usage.ipynb
    ./ipython_notebooks/simple_ml_model.ipynb
+   ./ipython_notebooks/GA_tutorial.ipynb
 
 .. toctree::
    :maxdepth: 2
diff --git a/docs/ipython_notebooks/GA_tutorial.ipynb b/docs/ipython_notebooks/GA_tutorial.ipynb
new file mode 100644
index 00000000..0e1da3a2
--- /dev/null
+++ b/docs/ipython_notebooks/GA_tutorial.ipynb
@@ -0,0 +1,109347 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Genetic Algorithm GUI tutorial\n",
+    "\n",
+    "A python implementation of real-valued, genetic algorithm for solving hyperparameter optimization problems.\n",
+    "\n",
+    "We recommend going through the Hyperparameter Optimization using `chemml.optimization.GeneticAlgorithm` section of the documentation to get a better understanding of the parameters required to run the Genetic Algorithm. In this tutorial we showcase the procedure to optimize the hyperparameters of a MLPRegressor model. \n",
+    "\n",
+    "We use a sample dataset from the ChemML library which has the SMILES codes and the HOMO energies (eV). We represent the data using Morgan Fingerprints (represented and a vector using 1024 bits). We split the data using a 90:10 (training data: testing data) split. We predict the HOMO energies of the test data set using the MLPRegressor model with optmized hyperparameters.\n",
+    "\n",
+    "Five text files containing python code are required to run the Genetic Algorithm using the GUI. \n",
+    "\n",
+    "Text files required:\n",
+    " - evaluate\n",
+    " - error_metric\n",
+    " - single_obj\n",
+    " - space\n",
+    " - test_hyperparameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### evaluate \n",
+    "\n",
+    "The file `ga_eval.txt` contains python code which evaluates every individual (tuple of hyperparameters) and yields the results of the error metric for each generation. Once model type is selected in the Wrapper parameters section of the GUI, the model is defined in this section code. For each generation, the hyperparemeters of the model are set and the model is passed to the objective function (`single_obj`) which evaluates the model. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The hyperparameter optimization process can be a time consuming one. Hence, for our convinience, we use a temporary file to guage and print the progress of the iterations. This is done using this section of the code: \n",
+    "\n",
+    "    #count iterations of GA\n",
+    "    count=open(\"tmp.txt\", \"a\")\n",
+    "    count.write(\"GA search iteration in process... \\n\")\n",
+    "    count.close()\n",
+    "    file = open(\"tmp.txt\",\"r\")\n",
+    "    Counter = 0\n",
+    "    # Reading number of lines from file\n",
+    "    Content = file.read()\n",
+    "    CoList = Content.split(\"\\n\")\n",
+    "    for i in CoList:\n",
+    "        if i:\n",
+    "            Counter += 1\n",
+    "    print(\"GA search iteration in process... \",Counter)\n",
+    "\n",
+    "\n",
+    "_Note: The `tmp.txt` file is deleted once the GA search is completed_\n",
+    "\n",
+    "\n",
+    "**`ga_eval.txt` has the following python code:**\n",
+    "\n",
+    "    def ga_eval(indi):\n",
+    "        \n",
+    "        layers = [indi[i] for i in range(2,5) if indi[i] != 0]\n",
+    "        #count iterations of GA\n",
+    "        count=open(\"tmp.txt\", \"a\")\n",
+    "        count.write(\"GA search iteration in process... \\n\")\n",
+    "        count.close()\n",
+    "        file = open(\"tmp.txt\",\"r\")\n",
+    "        Counter = 0\n",
+    "        # Reading number of lines from file\n",
+    "        Content = file.read()\n",
+    "        CoList = Content.split(\"\\n\")\n",
+    "        for i in CoList:\n",
+    "            if i:\n",
+    "                Counter += 1\n",
+    "        print(\"GA search iteration in process... \",Counter)\n",
+    "        mlp = MLPRegressor(alpha=np.exp(indi[0]), activation=indi[1], \n",
+    "        hidden_layer_sizes=tuple(layers),learning_rate='invscaling', max_iter=10,early_stopping=True)\n",
+    "        ga_search = single_obj(mlp=mlp, x=X.values, y=Y.values,n_splits=n_splits)\n",
+    "        f=open(\"GA.txt\", \"a\")\n",
+    "        f.write(\"%f %s %d %d %d %f \\n\" %(float(np.exp(indi[0])), str(indi[1]), int(indi[2]), int(indi[3]), \n",
+    "        int(indi[4]),float(ga_search)))\n",
+    "        f.close()\n",
+    "        return ga_search"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### error_metric\n",
+    "\n",
+    "The file `error_metric.txt` contains python code which returns the user defined error metric score (in this case Mean Absolute Error) for the data. The error metric returned in this function will define the criteria on which the \"best model\" is evaluated.\n",
+    "\n",
+    "    def error_metric(y_true,y_pred):\n",
+    "        y_true = np.asarray(y_true)\n",
+    "        y_pred = np.asarray(y_pred)\n",
+    "        ndata = len(y_true)\n",
+    "        y_mean = np.mean(y_true)\n",
+    "        e = y_true - y_pred\n",
+    "        ae = np.absolute(e)\n",
+    "        se = np.square(e)\n",
+    "        var = np.mean(np.square(y_true - y_mean))\n",
+    "        MAE = np.mean(ae)\n",
+    "        return MAE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### single_obj\n",
+    "\n",
+    "The file `single_obj.txt` contains python code which fits the model to the dataset using the hyperparameters defined in the `ga_eval` function. The training data is split into `n-splits` (defined by the user) and an error metric for each `fold` is obtained. \n",
+    "\n",
+    "This function returns the mean error_metric score of the `Kfold`(s) specified.\n",
+    "\n",
+    "\n",
+    "**`single_obj.txt` has the following python code:**\n",
+    "\n",
+    "    def single_obj(mlp, x, y, n_splits=n_splits):\n",
+    "        n_splits=n_splits\n",
+    "        kf = KFold(n_splits)                    # cross validation based on Kfold (creates 5 validation train-test sets)\n",
+    "        accuracy_kfold = []\n",
+    "        for training, testing in kf.split(x):\n",
+    "            mlp.fit(x[training], y[training])\n",
+    "            y_pred = mlp.predict(x[testing])\n",
+    "            y_pred, y_act =y_pred.reshape(-1,1), y[testing].reshape(-1,1)\n",
+    "            model_accuracy=mae(y_act,y_pred)                             # evaluation metric:  mae\n",
+    "            accuracy_kfold.append(model_accuracy)                         # creates list of accuracies for each fold\n",
+    "        #print(\"def single_obj - completed\")\n",
+    "        return np.mean(accuracy_kfold)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### space\n",
+    "\n",
+    "The file `space.txt` contains python code which defines the space variable.\n",
+    "\n",
+    "    space = ({'alpha': {'uniform': [np.log(0.0001), np.log(0.1)], 'mutation': [0, 1]}},{'activation': {'choice': ['identity', 'logistic', 'tanh', 'relu']}},{'neurons1':  {'choice': range(0,220,20)}},{'neurons2':  {'choice': range(0,220,20)}},{'neurons3':  {'choice': range(0,220,20)}})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### test_hyperparameters\n",
+    "\n",
+    "The file `test_hyperparameters.txt` contains python code which fits the model, having the best hyperparameters obtained using the GA, to the training dataset. The model is used to predict the values of the testing data set. The function returns a error metric score (in this case MAE) of the predicted values and actual values. \n",
+    "\n",
+    "**`test_hyperparameters.txt` has the following python code:**\n",
+    "\n",
+    "    def test_hyp(mlp, x, y, xtest, ytest):                                          \n",
+    "        mlp.fit(x, y)\n",
+    "        ypred = mlp.predict(xtest)\n",
+    "        acc=mae(ytest,ypred)\n",
+    "        # print(\" test_hyp completed \")\n",
+    "        return np.mean(acc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "605ad5f4ee024381a8c5139a57d118f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Accordion(children=(VBox(children=(Label(value='Choose how to start:', layout=Layout(width='50%')), Tab(childr…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The computation graph will be displayed here:\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Image(value=b'')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1576fe93d8674e558aebdf21fe3d3c4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Image(value=b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIHDR\\x00\\x00\\x032\\x00\\x00\\x02\\x7f\\x08\\x06\\x00\\x00\\x00\\xa3\\x84\\xe6…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "The ChemML Wrapper's config file has been successfully saved ...\n",
+      "    config file name: chemML_config.txt\n",
+      "    current directory: /mnt/c/Aatish/UB/Mr. Hachmann/master_chemml_wrapper_v2\n",
+      "    what's next? run the ChemML Wrapper using the config file with the following codes:\n",
+      "        >>> from chemml.wrapper.engine import run\n",
+      "        >>> run(INPUT_FILE = 'path_to_the_config_file', OUTPUT_DIRECTORY = 'CMLWrapper_out')\n",
+      "... you can also create a python script of the above codes and run it on any cluster that ChemML is installed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from chemml.wrapper.notebook import ChemMLNotebook\n",
+    "ui = ChemMLNotebook()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=================================================\n",
+      "=================================================\n",
+      "Thu Oct 21 13:15:40 2021\n",
+      "\n",
+      "parsing the input file: /mnt/c/Aatish/UB/Mr. Hachmann/master_chemml_wrapper_v2/chemML_config.txt ...\n",
+      "\n",
+      "1   Task: (Input,datasets)\n",
+      "        <<<<<<<\n",
+      "        host = chemml\n",
+      "        function = load_cep_homo\n",
+      "        >>>>>>>\n",
+      "        smiles -> send (id=0)\n",
+      "        homo -> send (id=4)\n",
+      "         :nothing to receive:\n",
+      "        \n",
+      "2   Task: (Output,file)\n",
+      "        <<<<<<<\n",
+      "        host = chemml\n",
+      "        function = SaveFile\n",
+      "        format = smi\n",
+      "        header = False\n",
+      "        filename = smiles\n",
+      "        >>>>>>>\n",
+      "        filepath -> send (id=1)\n",
+      "        df <- recv (id=0)\n",
+      "        \n",
+      "3   Task: (Represent,molecular descriptors)\n",
+      "        <<<<<<<\n",
+      "        host = chemml\n",
+      "        function = RDKitFingerprint\n",
+      "        >>>>>>>\n",
+      "        df -> send (id=2)\n",
+      "        df -> send (id=3)\n",
+      "        molfile <- recv (id=1)\n",
+      "        \n",
+      "4   Task: (Output,file)\n",
+      "        <<<<<<<\n",
+      "        host = chemml\n",
+      "        function = SaveFile\n",
+      "        filename = fps_rdkfp\n",
+      "        >>>>>>>\n",
+      "         :nothing to send:\n",
+      "        df <- recv (id=2)\n",
+      "        \n",
+      "5   Task: (Prepare,split)\n",
+      "        <<<<<<<\n",
+      "        host = sklearn\n",
+      "        function = train_test_split\n",
+      "        >>>>>>>\n",
+      "        dfx_train -> send (id=5)\n",
+      "        dfy_train -> send (id=6)\n",
+      "        dfx_test -> send (id=7)\n",
+      "        dfy_test -> send (id=8)\n",
+      "        dfx <- recv (id=3)\n",
+      "        dfy <- recv (id=4)\n",
+      "        \n",
+      "6   Task: (Optimize,genetic algorithm)\n",
+      "        <<<<<<<\n",
+      "        host = chemml\n",
+      "        function = GA\n",
+      "        algorithm = 3\n",
+      "        ml_model = MLPRegressor\n",
+      "        evaluate = ./chemml/chemml/datasets/GA_files/ga_eval.txt\n",
+      "        space = ./chemml/chemml/datasets/GA_files/space.txt\n",
+      "        error_metric = ./chemml/chemml/datasets/GA_files/error_metric.txt\n",
+      "        test_hyperparameters = ./chemml/chemml/datasets/GA_files/test_hyperparameters.txt\n",
+      "        single_obj = ./chemml/chemml/datasets/GA_files/single_obj.txt\n",
+      "        fitness = (<built-in function min>,)\n",
+      "        pop_size = 5\n",
+      "        crossover_size = 2\n",
+      "        mutation_size = 2\n",
+      "        n_splits = 5\n",
+      "        n_generations = 5\n",
+      "        >>>>>>>\n",
+      "        best_ind_df -> send (id=9)\n",
+      "        best_individual -> send (id=10)\n",
+      "        dfx_train <- recv (id=5)\n",
+      "        dfy_train <- recv (id=6)\n",
+      "        dfx_test <- recv (id=7)\n",
+      "        dfy_test <- recv (id=8)\n",
+      "        \n",
+      "7   Task: (Output,file)\n",
+      "        <<<<<<<\n",
+      "        host = chemml\n",
+      "        function = SaveFile\n",
+      "        filename = best_ind_df\n",
+      "        >>>>>>>\n",
+      "         :nothing to send:\n",
+      "        df <- recv (id=9)\n",
+      "        \n",
+      "8   Task: (Output,file)\n",
+      "        <<<<<<<\n",
+      "        host = chemml\n",
+      "        function = SaveFile\n",
+      "        filename = best_individual\n",
+      "        >>>>>>>\n",
+      "         :nothing to send:\n",
+      "        df <- recv (id=10)\n",
+      "        \n",
+      "=================================================\n",
+      "\n",
+      "======= block#1: (chemml, load_cep_homo)\n",
+      "| run ...\n",
+      "\n",
+      "| ... done!\n",
+      "| execution time: 4.78s (0h 0m 4.78s)\n",
+      "=======\n",
+      "\n",
+      "\n",
+      "======= block#2: (chemml, SaveFile)\n",
+      "| run ...\n",
+      "\n",
+      "| ... done!\n",
+      "| execution time: 0.05s (0h 0m 0.05s)\n",
+      "=======\n",
+      "\n",
+      "\n",
+      "======= block#3: (chemml, RDKitFingerprint)\n",
+      "| run ...\n",
+      "\n",
+      "| ... done!\n",
+      "| execution time: 1.83s (0h 0m 1.83s)\n",
+      "=======\n",
+      "\n",
+      "\n",
+      "======= block#4: (chemml, SaveFile)\n",
+      "| run ...\n",
+      "\n",
+      "| ... done!\n",
+      "| execution time: 0.14s (0h 0m 0.14s)\n",
+      "=======\n",
+      "\n",
+      "\n",
+      "======= block#5: (sklearn, train_test_split)\n",
+      "| run ...\n",
+      "\n",
+      "| ... done!\n",
+      "| execution time: 0.03s (0h 0m 0.03s)\n",
+      "=======\n",
+      "\n",
+      "\n",
+      "======= block#6: (chemml, GA)\n",
+      "| run ...\n",
+      "\n",
+      "Hyperparameter optimization is a time consuming process - do not shutdown Kernel....\n",
+      "\n",
+      "Total GA search iterations =  25\n",
+      "GA search iteration in process...  1\n",
+      "GA search iteration in process...  2\n",
+      "GA search iteration in process...  3\n",
+      "GA search iteration in process...  4\n",
+      "GA search iteration in process...  5\n",
+      "GA search iteration in process...  6\n",
+      "GA search iteration in process...  7\n",
+      "GA search iteration in process...  8\n",
+      "GA search iteration in process...  9\n",
+      "GA search iteration in process...  10\n",
+      "GA search iteration in process...  11\n",
+      "GA search iteration in process...  12\n",
+      "GA search iteration in process...  13\n",
+      "GA search iteration in process...  14\n",
+      "GA search iteration in process...  15\n",
+      "GA search iteration in process...  16\n",
+      "GA search iteration in process...  17\n",
+      "GA search iteration in process...  18\n",
+      "GA search iteration in process...  19\n",
+      "GA search iteration in process...  20\n",
+      "GA search iteration in process...  21\n",
+      "GA search iteration in process...  22\n",
+      "GA search iteration in process...  23\n",
+      "GA search iteration in process...  24\n",
+      "GA search iteration in process...  25\n",
+      "GeneticAlgorithm - complete!\n",
+      "\n",
+      "\n",
+      "genetic algorithm results for each generation: \n",
+      "                             Best_individual  Fitness_values  Time (hours)\n",
+      "0  (-7.483401552230648, tanh, 140, 80, 120)        0.390616      0.008216\n",
+      "1  (-7.483401552230648, tanh, 140, 80, 120)        0.390616      0.009518\n",
+      "2  (-7.483401552230648, tanh, 140, 80, 120)        0.390616      0.010988\n",
+      "3  (-7.483401552230648, tanh, 140, 80, 120)        0.390616      0.011194\n",
+      "4  (-7.483401552230648, tanh, 140, 80, 120)        0.390616      0.010027 \n",
+      "\n",
+      "best particle:  {'alpha': -7.483401552230648, 'activation': 'tanh', 'neurons1': 140, 'neurons2': 80, 'neurons3': 120} \n",
+      "\n",
+      "Calculating accuracy on test data....\n",
+      "\n",
+      "\n",
+      "Test set error_metric (default = MAE) for the best GA hyperparameter:  0.7548308245753313 \n",
+      "\n",
+      "| ... done!\n",
+      "| execution time: 216.86s (0h 3m 36.86s)\n",
+      "=======\n",
+      "\n",
+      "\n",
+      "======= block#7: (chemml, SaveFile)\n",
+      "| run ...\n",
+      "\n",
+      "| ... done!\n",
+      "| execution time: 0.03s (0h 0m 0.03s)\n",
+      "=======\n",
+      "\n",
+      "\n",
+      "======= block#8: (chemml, SaveFile)\n",
+      "| run ...\n",
+      "\n",
+      "| ... done!\n",
+      "| execution time: 0.04s (0h 0m 0.04s)\n",
+      "=======\n",
+      "\n",
+      "\n",
+      "Total execution time: 223.80s (0h 3m 43.80s)\n",
+      "2021-10-21 13:19:23\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from chemml.wrapper.engine import run\n",
+    "run(INPUT_FILE = '/mnt/c/Aatish/UB/Mr. Hachmann/master_chemml_wrapper_v2/chemML_config.txt', OUTPUT_DIRECTORY = 'ga_out')"
+   ]
+  }
+ ],
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FamtrUVVV1WzcezkAvVvW29ezsLCAnp4eGhoaIIRo8TVq1CiNz5WWlrY4jXVRURGA3wvNw9DW30FH3eaDVFNTg5ycHDg5OUkdhYiI6K5YZOihefTRR+Ho6Ij//ve/Uke5o7feegvp6enYs2cP5HL5Ax170qRJAIB9+/ZpLK+rq8OhQ4dgZGSEMWPGoKmpCYmJibC1tcXixYthZWWlnvb59tnCbrl1+detS8xuKSkpQUZGxn1l3b9/f7P3/Pz88PLLLwMAJk+ejMbGxhZnHPvggw/Qq1cvNDY2aiyvra1FSkqKxrIzZ84gPz8fPj4+93Q2prVa+zvo6Nt8ULZt24aGhoZml8URERFpJanuzqGu4fPPPxf6+vrixIkTUkdp5ptvvhEA/vSVnJzcqrFaM2tZRUWFxuxVX331lXrd0aNHCwBi1apVori4WFRXV4tffvlF9OrVSwAQsbGx6nUvXrwounfvrjFrWXp6uhgzZoywtra+r1nL7OzsRHR0tKioqBDXrl0Tzz33nLCxsRFXrlwRQghx/fp14ebmJlxdXcX+/ftFeXm5KC0tFevWrRPGxsbNJgvw8fER5ubmIigo6IHPWlZTU6OxfNmyZc0mFmjL7+DPxh4zZozQ1dVtlmXEiBHCxMSkxZ9la7epLQoKCoS1tbV44YUXpI5CRETUGpy1jB6upqYmERISIuzt7UV2drbUcTSMGzfuvovM6tWrm31m+fLl6vdLSkrEkiVLhIuLi9DX1xfm5uZizJgx4tChQxrjFBcXi4ULFwpHR0ehr68vbGxsxJw5c8Trr7+uHvf2GcEyMjJEeHi4MDMzE0ZGRmLgwIEiOjpaBAUFqdd/5pln2vTz+GNWOzs7MX36dJGZmamxXmlpqXjllVeEq6ur0NfXF1ZWViI0NFSjbN1yq+CdO3dOjBkzRigUCmFkZCRGjBghEhIS2pQvOTn5jj/rPy4fN27cHferpd/BncZOSUlptnzlypXi6NGjzZa/+eabbdqmNrlx44Z49NFHRZ8+fTRmyCMiItJih2VCtHDxOtEDVFFRgdGjRyMvLw8//vhjs9mmqPPy9fVFSUlJs5nMSHvk5ORg/PjxUCqVOHLkCFxcXKSORERE1BrxvEeGHjozMzPExcXB19cXgYGBWLNmTYtPqyei9rV9+3b4+flBT08PycnJLDFERNShsMhQu1AoFNi/fz9WrVqF5cuXo3///oiOjpY6FlGXdO7cOYwfPx7Tp09HWFgYkpKSNKbMJiIi6ghYZKjdyGQyvPTSS0hLS4OXlxfCwsIQEhKC1NRUqaN1WjKZ7K6vt95664Fvd82aNZDJZEhNTUVeXh5kMhn+8Y9/aF3OriYvLw8LFy6Et7c3ioqKEB8fj02bNjV7Pg4REVFHwHtkSDKHDx/G0qVLcerUKUyYMAHPP/88goOD1VMPE9GDceLECXz++efYtm0b7OzssGLFCkydOpX/1oiIqCPjPTIknZEjRyIlJQXbt29HWVkZQkND8cgjj+CTTz5RP0GeiO5NXV0dvvvuOwwePBgDBgxASkoKPvroI5w7dw7Tpk1jiSEiog6PZ2RIa5w5cwaff/45Nm/eDACYMGECIiIi8Pjjj8PQ0FDidETar6mpCYcPH8bOnTvxww8/QKlUYvLkyXj++ecxfPhwqeMRERE9SPEsMqR1lEoltmzZgu3btyMhIQEmJiYICwvDlClTMHbsWJYaots0NTUhLi4OO3fuRFRUFIqLi+Hn54epU6di9uzZsLOzkzoiERHRw8AiQ9qtoKAAu3btws6dO3H06FEYGxtj9OjRCA4ORkhICPr27St1RKJ2d/XqVRw8eBCxsbE4ePAgSkpK4Ofnh4iICERERKB3795SRyQiInrYWGSo4ygsLMTu3bsRExODuLg4lJeXw9HRESEhIQgODkZQUBCsra2ljkn0wFVWViIuLg6xsbGIjY1FRkYGjIyMEBgYiJCQEEyaNInlhYiIuhoWGeqYmpqacPr0aRw8eBAHDx7EkSNHUF9fDzs7OwQGBmLo0KHw9/fHY489Bn19fanjErVJfn4+EhMTkZCQgBMnTiAlJQX19fVwdXVFcHAwxo8fj5CQEF5mSUREXRmLDHUOlZWVOHLkCI4dO4akpCSkpKSgsrISCoUCgwYNQkBAAAYNGgRvb284OTlJHZdIraioCKmpqUhJScGxY8eQnJyMkpISyOVy+Pv7Y/DgwQgICMDw4cN5xpGIiOh/WGSoc2pqakJ6ejqSkpJw7NgxHDt2DJmZmRBCoFu3bvDx8YG3tzd8fHzg4+MDLy8v/t9teqgaGxuRkZGB1NRU9SstLQ0FBQUAgJ49eyIgIAABAQEYPHgw/P39YWBgIHFqIiIircUiQ11HRUUF0tLSkJaWhtTUVJw+fRpnz55FdXU19PT04OHhgb59+8LDw0Pja0tLS6mjUwdSWVmJzMxMZGZmIiMjAxkZGcjMzER6ejrq6upgYGCARx55pFmZtrKykjo6ERFRR8IiQ11bU1MTLl68qC44tw46MzMzUVNTAwCwtLREnz590KdPH3h4eMDFxQVOTk5wcnLi1LZdVGlpKa5cuYIrV67g8uXLGsUlPz8fAKCvrw9XV1d1Ie7fvz+8vb3h6enJ+7aIiIjuH4sMUUuEELh69ar6APXChQvIyMhAVlYWrl27hqamJgCAoaEhnJyc0KtXL3W5cXJygouLC2xtbWFvbw9jY2OJ94baoq6uDoWFhcjLy8O1a9fUheXKlSvIycnBlStXUFVVBQCQyWSws7NTn8Xz8PBQl14XFxfo6elJvDdERESdFosMUVs1NjYiNzdX4//I336we+3aNdTX16vXVygUcHBwgI2NDXr27Alra2v19w4ODrCyskL37t1haWkJuVwu4Z51Xo2NjSgtLVW/8vPzUVhYiPz8fBQUFKi/LiwsRGlpqfpzurq6cHBwgJOTE5ydnTXK6q0Xf2dERESSYJEhetBUKpXGgXFBQQHy8/Nx/fp15OXlqf8sKirSKDwAYGJiAktLS1haWqJHjx6wtLRUlxxLS0uYmZnB1NQU3bp1g6mpqfplbm4Oc3Nz6OjoSLTXD9+NGzdQVVWlfimVSlRUVKCqqgoVFRXqklJWVoa8vDwUFxejpqYGJSUlqKio0BhLV1cX1tbWsLOzg52dHWxtbeHg4ABra2uNsmlnZ8fLwIiIiLQTiwyRlIqKijQOwG//uri4WL3s1p+3DtzvxNjYGKampjAxMUH37t0BABYWFpDJZDAzM4Ouri5MTExgYGAAQ0NDGBkZQV9fH6ampuoxDAwMYGJi0uL4dypL5eXlaOk/JbW1tep7jQCgpqYGtbW1qK+vx82bN9HU1ISKigoIIVBeXg4AUCqVUKlU6n2tqqpCZWXlHffZyMgIZmZmGqXv4sWLyMzMxKRJkxASEtKsEFpbW0NXV/eOYxIREZHWi+cF3EQSsra2vqdng9w6O1FZWak+I1FeXo6qqioUFhbi448/hqOjI9zd3aFUKgH8r2wUFhaisbER1dXVqKurQ11dHaqrq9Vj37x5s9mZIgDq0tGSW+Xoj3R1dWFmZqb+Xi6Xw9jYGHp6elAoFJDJZOqi5ezsDB0dHZiamqrLlUKhgKmpKczMzGBhYaE+A6VQKGBubq4uZy1lXb58OVatWgULCwv85z//4VTGREREnQzPyBB1IuXl5QgKCkJpaSni4+O7/MM/IyMjMXfuXHh7eyMqKopTHBMREXUe8Z33gnqiLqa8vBwhISEoKSlBXFxcly8xADBlyhQcO3YMBQUFeOyxx5Ceni51JCIiInpAWGSIOgGlUonQ0FBcv34dcXFxcHFxkTqS1vD09MSvv/4KBwcHDBkyBAcOHJA6EhERET0ALDJEHVxFRQXGjBmDgoICxMXFwdXVVepIWqdHjx44ePAgwsPDERYWhk2bNkkdiYiIiO4Tb/Yn6sBu3ryJsLAw5OTkIC4uDm5ublJH0lpyuRzffvstPD09MWfOHBQVFWHp0qVSxyIiIqJ7xCJD1EFVV1dj/PjxuHDhAuLi4vDII49IHalDWLZsGfT19bF06VKUl5fjvffekzoSERER3QMWGaIO6FaJSU9PR1xcHDw9PaWO1KG88sorsLS0xLx581BbW4s1a9ZIHYmIiIjaiEWGqIOpqalBWFgYzpw5g19++QVeXl5SR+qQZs+eDWNjYzz11FMQQmDt2rVSRyIiIqI2YJEh6kDq6+sxZcoUnDx5EgcPHkT//v2ljtShRUREQAiBGTNmQEdHB6tXr5Y6EhEREbUSiwxRB1FfX48nnngCiYmJOHjwIPz9/aWO1ClMnToVtbW1+Otf/4oePXpg2bJlUkciIiKiVmCRIeoAbpWYo0ePIjY2FgMGDJA6Uqfy9NNPo7q6Gs8//zxsbW0xe/ZsqSMRERHRXbDIEGm5pqYmzJo1C/Hx8YiJicHAgQOljtQpPfvss7h8+TLmz58PW1tbjBkzRupIRERE9CdkQgghdQgiapkQAs8++yw2btyIvXv3IjQ0VOpInZoQAjNnzkR0dDSSk5M5GxwREZH2imeRIdJiy5Ytw9q1a7F9+3Y88cQTUsfpEurr6zFq1CiUlJTg+PHjMDc3lzoSERERNRevI3UCImrZe++9h9WrV+Prr79miWlHBgYG+OGHH3Dz5k3MmjULKpVK6khERETUAhYZIi30+eef45///Cc+/PBD/PWvf5U6Tpdja2uL7du348CBA1ixYoXUcYiIiKgFLDJEWmbLli1YtGgR3n//fSxZskTqOF3W0KFDsWbNGrz99tv49ddfpY5DREREf8B7ZIi0yM8//4ywsDAsWbIEq1atkjpOlyeEwLhx45CZmYnTp0/D1NRU6khERET0O97sT6QtTpw4gZEjR2LChAnYvHkzZDKZ1JEIQH5+Pvr374/p06fjs88+kzoOERER/Y5FhkgbXLp0CUOHDoWnpyd++uknGBgYSB2JbvP999/jqaeewqFDhzBq1Cip4xARERGLDJH0SkpKEBgYCFNTUxw+fJiXL2mpsLAwXLp0CadPn4a+vr7UcYiIiLo6Tr9MJKXq6mpMmDABDQ0NiI6OZonRYv/+979x6dIlXl5GRESkJVhkiCTS2NiIiIgIXLx4EQcOHICtra3UkehPuLm5YenSpXjzzTdRUFAgdRwiIqIuj0WGSCIvvvgi4uPjER0dDXd3d6njUCv8/e9/h7m5Od59912poxAREXV5LDJEEli7di2+/vprbN68GYMGDZI6DtasWQOZTAaZTIaePXtKHaddfP/99+p9NjQ0bHGd7du3w9fXF0ZGRpDJZDAxMcHcuXOxfv16XLp0qZ0TExER0e1YZIja2f79+7Fs2TKsWrUK4eHh7b79qqoquLu7Y/z48eplS5cuhRACPj4+7Z5HKtOnT4cQAkFBQS2+n5iYiCeffBKhoaEoLi7GxYsX0bNnT0yaNAkuLi54//332zkxERER3Y5FhqgdnTp1CtOmTcPs2bPx6quvSpJBCAGVSgWVSiXJ9juKnTt3QgiBl156CaampnBzc8O1a9fg4+OD5cuX49tvv0VGRobUMYmIiLosFhmidlJQUICJEyfC398fX3zxhWQ5FAoFsrOzsX//fsky/Bk/Pz988MEHuHr1qqQ5rl27BgCwtLRs9t6MGTPg4eHBe2WIiIgkxCJD1A5qamoQHh4OExMTREVF8YGXf8LOzg7Lly+Hs7MzRo4cia+//hrl5eXtnqOpqemO7+nq6uKtt97Ctm3bkJaW1o6piIiI6BYWGaKHTKVS4amnnkJ2djb27t2Lbt26abxfV1eHN954A3379oWxsTG6d++OsLAw/Pjjj+qD6d27d6tvTJfJZLhy5QqmTZsGhUIBS0tLzJo1Czdu3EBOTg7CwsKgUChgZ2eH+fPno7KyUr2tP45TW1vbqn0oLi7G4sWL4ezsDAMDA1hZWWHy5Mk4ffp0m/flbvbv34/c3FysWbMGFRUVWLBgAWxtbTF58mTs2rULdXV1rRrnjy5cuIDw8HCYmwt6pFkAACAASURBVJvDxMQEw4YNQ0JCQrP1bv2M9uzZAwDqG/0HDx6ssV5ERAS8vb15VoaIiEgqgogeqldffVUYGBiIw4cPt/j+vHnzhLm5uYiJiRHV1dWisLBQLF26VAAQcXFxGutOnDhRABCTJ08Wv/32m6iqqhKbNm0SAMTYsWPFxIkTxalTp0RlZaVYt26dACBefvnlZtu8NU5NTY3Gch8fH+Hg4KCxLD8/Xzg5OQkbGxuxb98+UVlZKc6ePStGjBghDA0NRVJS0j3tS2udO3dO/P3vfxe9evUSAISFhYWYN2+eiIuLEyqVqlVjZGVlCQsLC+Hg4CBiYmJEZWWlSEtLE6GhocLZ2VnI5fJmn7nTz+h2u3fvFjKZTBw/fvye9o2IiIju2WEWGaKH6L///a+QyWRiy5Ytd1zHxcVFDBkypNlyDw+POxaZffv2aSz38vISAER8fHyzsfv06dNs7LYUmdmzZwsAzfahoKBAyOVy4e/vf0/70lYqlUocPnxYzJs3T1hYWAgAwtvbu1WfjYiIEABEZGSkxvK8vDwhl8vvucioVCoxcOBAMWnSpLbtDBEREd2vw7y0jOghSU5OxvPPP4/ly5fjqaeeuuN6jz/+OJKSkrBgwQIcO3ZMfQlWRkYGRo4c2eJnBgwYoPG9vb19i8sdHByQn59/H3vx+6VWOjo6GtM1A4CtrS28vLxw4sQJ5Obm3vO+tNat570YGhpCX1+/TZ89cOAAAGDMmDEay+3t7eHh4XFfmV5//XXs3r0b586du+dxiIiIqO1YZIgegoKCAkRERGDUqFF46623/nTdzz77DJs2bcKlS5cQFBQEMzMzPP7444iKirrjZ8zMzDS+19HRga6uLoyNjTWW6+rq3tc0y3V1dVAqlVCpVDA3N9e4v0Ymk+HkyZMAgKysrHvel7vJzMzEm2++id69e2Pw4MHYvHkzJkyYgF9++aXZPTp32ofKykoYGhrC1NS02fvW1tb3nA0AwsPD0bdvX3z44Yf3NQ4RERG1DYsM0QNWW1uLSZMmwdTUFN9//z10dXX/dH2ZTIZZs2bh4MGDKC8vx+7duyGEwOTJkyU/OJbL5bCwsICenh4aGhoghGjxNWrUqAe6L9evX8cnn3yCQYMGoU+fPvjXv/6F/v37IzIyEoWFhVi/fj1GjRoFmUzWqn1QKBSora1FVVVVs/fLyspa/wNpgY6ODl555RVs2rRJPWUzERERPXwsMkQP2Isvvojz588jKioK5ubmd13fwsICFy5cAADo6+sjJCREPXPWvn37Hnbcu5o8eTIaGxuRmJjY7L0PPvgAvXr1QmNjI4AHsy/jxo2Dg4MDlixZAiMjI3z55ZcoLCxEVFQUnnjiCcjl8jbvw9ixYwH87xKzW0pKSh7IQy1nzZoFKysr/Oc//7nvsYiIiKh1WGSIHqAPP/wQ33zzDbZt24ZHHnmk1Z979tlnkZaWhrq6OhQVFWHVqlUQQmD06NEPMW3rrFy5Em5ubpg7dy5++uknKJVKlJWV4csvv8Q777yDNWvWQE9PT73+/e5Lfn4+3nvvPeTk5CA+Ph4LFixoNmV1W61YsQLdu3fHkiVLEBsbi6qqKpw7dw4zZ85s8XKztpLL5XjppZewbt06SZ55Q0RE1CVJNc0AUWcTGxsr9PT0xKpVq9r0udOnT4uFCxeKRx55RBgbG4vu3buLwYMHi6+//lo9vXBycrIAoPFavny5SElJabZ85cqV4ujRo82Wv/nmmyIqKqrZ8hkzZojVq1e3OP4tpaWl4pVXXhGurq5CX19fWFlZidDQUBEbG9vmfZFKRkaGCA8PF2ZmZsLIyEgMHDhQREdHi6CgIPU+P/PMMy3+jACI5OTkPx1fqVQKCwsLsXLlynbaIyIioi7tsEwIIdqpMxF1Wjk5ORg4cCDGjBmDzZs3Sx2HJPL6669j48aNuHz5MoyMjKSOQ0RE1JnFs8gQ3aeqqioEBARALpfj6NGjPIDtwgoKCuDi4oLPPvsMzzzzjNRxiIiIOrN43iNDdJ/mzZuHoqIiREVFscR0cXZ2dpg6dSpv+iciImoHLDJE92Ht2rWIjIzEd999B0dHR6njkBZ44YUXkJqaiuTkZKmjEBERdWq8tIzoHiUlJWHkyJFYsWIFli5dKnUc0iIDBw5Enz59eL8UERHRw8N7ZIjuRWFhIR599FEMGjQIUVFRrXowI3UdGzZswHPPPYerV6/CxsZG6jhERESdEe+RIWqrhoYGREREwMzMDJs2bWKJoWaefPJJKBQK/Pe//5U6ChERUafFIkPURkuWLMHp06exa9cumJmZSR2HtJCRkRHmzJmDdevWobGxUeo4REREnRKLDFEbbNmyBV988QU2bNgAT09PqeOQFnvuueeQl5eH6OhoqaMQERF1SrxHhqiV0tLSEBAQgBdffBEffPCB1HGoA/jLX/6CxsZGxMTESB2FiIios+HN/kStUV5ejgEDBsDJyQk///wz9PT0pI5EHcCePXswefJkZGdnw9nZWeo4REREnQlv9ie6GyEEnnnmGVRVVWHz5s0sMdRq48aNg7W1NTZt2iR1FCIiok6HRYboLj788EP8+OOP2LFjB+zs7KSOQx2Inp4ennzySWzcuBE8+U1ERPRgscgQ/Ylff/0V//d//4f3338fw4cPlzoOdUBz587F5cuXceTIEamjEBERdSq8R4boDsrKyvDoo4+iX79+2Lt3L58XQ/fM398fPj4+2LBhg9RRiIiIOgveI0O0cuVKVFVVaSxTqVSYMWMGhBD49ttvWWLovsyePRs7duxo9veMiIiI7h2LDHVpV65cwfLly+Hr64vU1FT18vfffx+HDh3C999/D0tLSwkTUmcwY8YMNDY24ocffpA6ChERUafBIkNd2o8//ghdXV3k5ORg4MCB+OKLLxAXF4e3334bH374IQICAqSOSJ2ApaUlxo8fj40bN0odhYiIqNPgPTLUpY0YMQIJCQlQqVQAAJlMhu7du2PYsGGIioqSOB11JtHR0ZgwYQIuX74MJycnqeMQERF1dLxHhrqu8vJyJCYmqksM8PszY5RKJY4dO4akpCQJ01FnM2bMGHTr1g07duyQOgoREVGnwCJDXVZ0dHSLz/ZobGxEcXExhg0bhrfeekuj6BDdK319fYSHh2Pnzp1SRyEiIuoUWGSoy9q9ezd0dFr+J9DU1ASVSoW3334bYWFhUCqV7ZyOOqOIiAikpKQgOztb6ihEREQdHosMdUl1dXXYv38/Ghsb77iOnp4eDA0NMW7cOJiZmbVjOuqsgoODYWVlhcjISKmjEBERdXgsMtQlHTp0CDU1NXd8X0dHB35+fkhNTcXzzz/P58jQA6Gnp4eJEyfy8jIiIqIHgEWGuqQ9e/bAwMCg2XI9PT3o6+tjxYoVSE5OhoeHhwTpqDOLiIjAiRMncPHiRamjEBERdWgsMtTlCCEQFRWF+vp6jeW6urpwd3dHSkoKli1bBl1dXYkSUmc2evRoWFtb86wMERHRfWKRoS7n+PHjKC4uVn+vp6cHHR0dLF26FKdPn4aPj4+E6aiz09PT4+xlREREDwCLDHU5e/bsgb6+PoDfDypdXV1x/Phx/Otf/2rxcjOiB23KlCk4deoULl++LHUUIiKiDktP6gBEdXV1qK6uRnl5Oaqrq1FbW4v6+nrcvHlTY73Kyspms4wpFAro6f3vr7GOjg7Mzc0BAN26dYORkRGMjY3VywAgMjISDQ0N0NHRwSuvvIJ33nkHcrn8Ie4hkaaRI0fCwsIC0dHRWLRokdRxiIiIOiSZaOmJgET3qLCwEHl5eSgoKEBpaSlKS0tRUlKC4uJilJSUqJdVVlaiqqoKFRUVaGpqapdsJiYmkMvlKCsrg1wuh5+fH9zc3NCjRw9YWlrC0tISVlZWsLa2hr29PRwdHWFoaNgu2ajrmTp1KsrLyxETEyN1FCIioo4onmdkqNVUKhWuXbuGrKwsZGVl4erVq8jNzVX/mZeXh7q6OvX6RkZGsLS0RI8ePWBlZYUePXrA19cXlpaWUCgUMDU1hZmZGYyMjGBiYgJzc3P1GRRdXd1mz24xMjJqVizKy8txexe/dSZHCKE+w1NTUwOlUomqqipER0cjNzcXAQEBqKioQGlpKXJyctQlq6SkRGN8KysrODg4wNHREY6OjujZsyfc3Nzg7u6O3r17Q6FQPISfNHUFYWFheOaZZ6BUKjXOGBIREVHr8IwMNVNbW4uzZ8/i9OnTyMzMVBeXixcvqotKt27d4OTkpD7Av/1g38HBAQ4ODjA2NpZ4T5orKCiAnZ3dHd9XqVQoKipCXl4e8vLycPXqVeTl5WkUtqtXr6ovcbO1tYWHh4e62PTv3x/e3t5wdHRsr12iDqqsrAw2NjbYtm0bpkyZInUcIiKijiaeRaaLKy0tRUpKClJTU3H69GmkpaUhMzMTjY2NMDU1RZ8+fdQH6e7u7vDw8EDv3r3Ro0cPqaNLpqGhAZcvX0ZWVhYyMzNx8eJFddnLyckBAHTv3h2+vr7w8fGBt7c3Hn30UXh5eXFKZ9IwbNgwuLm5YePGjVJHISIi6mhYZLqaS5cuISEhAYmJiUhISMCFCxegUqlgZ2cHLy8veHp6wt/fH/7+/ujbty8PvNuooqICaWlpOHfuHNLT03HixAmcOnUK1dXVMDU1hY+PDwIDAzF06FAMGTIElpaWUkcmCa1atQqrVq3C9evX+W+NiIiobVhkOruLFy8iJiYGMTExOHz4MJRKJUxMTDBw4EAMHToUAQEBGDx4MA+oH6Kmpiakp6cjKSkJSUlJSE5OxsWLF6GjowNPT0+EhoYiNDQUw4cPh5GRkdRxqR2dP38enp6eSExMxJAhQ6SOQ0RE1JGwyHQ2NTU1iI2NxYEDBxATE4Ps7GwoFAqMHj0aQUFBGDp0KLy9vTWmLKb2V1RUhOTkZMTHxyMmJgbp6ekwMjLCsGHDEBoaivHjx6NPnz5Sx6R24O7ujilTpmDlypVSRyEiIupIWGQ6g9raWsTGxmLnzp3Ys2cPqqqq4Ofnh+DgYAQHB2P48OF80KOWu379Oo4cOYKDBw8iOjoa+fn58PT0REREBKZOnQpPT0+pI9JDsnjxYiQkJODkyZNSRyEiIupIWGQ6KpVKhdjYWGzYsAH79u1DbW0thg8fjoiICEyaNAm2trZSR6R7pFKpcPToUezcuRO7du1CQUEB+vXrhyeffBJz587l77aT+fHHHzFp0iQUFhbCyspK6jhEREQdBYtMR3P9+nV88803+Prrr3H58mUMHz4cTz75JCZNmgRra2up49EDplKpkJCQgJ07d2Lbtm2oqKjAxIkTsXDhQgQFBUEmk0kdke5TZWUlLC0tsXnzZkydOlXqOERERB0Fi0xHce7cObz//vuIjIyEiYkJZs+ejYULF6Jv375SR6N2Ultbi8jISKxbtw6JiYno3bs3XnvtNcyZM4eXDnZwQ4cOhZeXF7766iupoxAREXUU8TpSJ6A/l56ejunTp6N///5IS0vDV199hby8PHz00UcsMV2MoaEhZs6ciYSEBJw5cwZBQUFYtGgR3N3dsW7dOtTX10sdke5RcHAwYmJipI5BRETUobDIaKnCwkLMmDED3t7eOHfuHLZv347U1FTMnj2bU/QS+vXrh3Xr1uHixYsYP348lixZAnd3d+zYsUPqaHQPgoODceXKFWRnZ0sdhYiIqMNgkdEyQgh89dVXeOSRR5CcnIwdO3YgNTUVU6ZMgY4Of12kydHREZ999hkuXryIkJAQTJ8+HePGjUNOTo7U0agNAgICYGZmhoMHD0odhYiIqMPgkbEWyc3NxfDhw/HCCy9g3rx5OHv2LJ544gne0E131bNnT6xfvx7x8fHIyclBv3798Pnnn0sdi1pJT08Pw4YNw6FDh6SOQkRE1GGwyGiJ5ORkDBw4EDdu3EBKSgpWr14NY2PjB74dIQQSExPxwgsvwMPDA3K5HNbW1ggMDMTmzZvRlrkfmpqasG7dOgwZMgTm5ubQ19eHvb09/vKXv+DTTz/VyrMCpqamkMlkrXqtX7/+juOsWbNGvV7Pnj3bcQ/+3LBhw3Dq1Cm8+uqrWLRoERYsWMB7ZzqI4OBgHDp0CCqVSuooREREHQKLjBbYvHkzRo0ahYEDByI5ORm+vr4PbVsZGRkIDAxEZmYmIiMjoVQqcezYMfTq1QuzZs3Ca6+91uqxZs2ahRdeeAHh4eFIT09HZWUljh49Cj8/PyxevBgDBgx4aPtxr6qqqnDq1CkAwMSJEyGEaPE1YsQIjc+4u7tj/Pjx6mVLly6FEAI+Pj7tvg93Y2BggLfffhtRUVH4/vvvERwcjLKyMqlj0V2MHDkSZWVlOHv2rNRRiIiIOgQWGYlt3boVs2fPxuLFi7F7924oFIqHvk09PT3s2LED3t7eMDQ0hKurKzZu3AhLS0t8+umnqKuru+sYKSkp2LZtG5555hn87W9/Q8+ePWFoaAg3Nze8//77eO655x76frQXIQRUKlWH+z/lEyZMQFJSEq5cuYKxY8eiqqpK6kj0J7y9vWFubo6EhASpoxAREXUILDIS+u233zB37ly8/PLLWLVqVbvczN+3b180NDSgW7duGssNDAzg6OiIuro61NbW3nWc9PR0AECfPn1afL+jP9jv8OHDmDdvHgBAoVAgOzsb+/fvlzhV2/Xr1w8HDx7ElStXMHfuXKnj0J/Q0dFBQEAAiwwREVErschIpL6+HrNmzcKwYcOwatUqqeOgvLwcWVlZ8PPzg7m5+V3Xt7GxAQDExsa2+P6IESNQUlLyQDO2hxdffBFLliyROsYD5e7ujm3btmHXrl347rvvpI5DfyIwMBBHjhyROgYREVGHwCIjkS+//BJXr17F+vXrJZ1WuaKiAomJiZgwYQJsbW2xadOmVn1u2LBhsLW1xc8//4yxY8fi8OHDd730qrGxEdu3b0dISAhsbW1hZGSE/v374+OPP1Z/try8vNlN9++9957687cvnzJlinrs4uJiLF68GM7OzjAwMICVlRUmT56M06dP3+NP5ne7d+/W2GZrzlY9zDz3atSoUViwYAFef/113vyvxQIDA5GXl6eVE2UQERFpHUGS8PDwEM8//7ykGd59910BQAAQI0eOFGlpaW36/NGjR4Wjo6N6DGtrazFjxgyxdetWcfPmzWbr7927VwAQK1asEGVlZaK4uFh88sknQkdHRyxdulRj3ccff1zo6OiIixcvNhsnICBAbN26Vf19fn6+cHJyEjY2NmLfvn2isrJSnD17VowYMUIYGhqKpKQkjc+fOnVKnbml10svvdRsmxMnThQARE1NjcZyHx8f4eDgoLGsrXnaS25urtDX19f42ZF2qampEXK5XHz33XdSRyEiItJ2h1lkJHDx4kUBQBw9elTqKKKurk6cP39ePPvss0JXV1e88847bfp8bW2t+Pbbb8XEiROFQqFQlwFLS0uxbds2jXX37t0rRo4c2WyMmTNnCn19faFUKtXLDh48KAA0K3sJCQmiV69eoqGhQb1s9uzZAoDYsmWLxroFBQVCLpcLf39/jeW3iszEiRObZXnhhRfuu8i0NU97Cg4OFjNnzpRs+3R3Q4YMEQsXLpQ6BhERkbY7zEvLJHDu3DkAgL+/v8RJfr/Jv2/fvvjiiy8wYcIEvPHGG216urhcLsfTTz+N3bt3o6ysDIcOHcL06dNRWlqKmTNnqqc6BoDx48cjLi6u2Rg+Pj5oaGhQTyAAAEFBQfDz88PGjRtRWlqqXr569WosWbIEenp66mW7d++Gjo6OxvTIAGBrawsvLy+cOHECubm5rd6n+6VteW7n7++v/vtH2ikwMBBHjx6VOgYREZHWY5GRwM2bN6GrqwtDQ0Opo2gICwsDAERHR9/T5/X09DB69Ghs27YNy5YtQ1NTEyIjI9XvK5VKvPHGG+jfvz+6deumvu/k1rNrqqurNcZ79dVXUV1drX5CfWZmJo4cOaKeTQwA6urqoFQqoVKpYG5u3uz+mpMnTwIAsrKyWrUPn376Kf7973/f0/4/jDwPmqmpKadh1nKBgYE4f/68RoEnIiKi5lhkJGBlZYWmpiYUFxdLHUWDXC4HgFY9PDExMVE9c1lLRo0aBQC4ceOGellYWBjeffddzJ8/H5mZmVCpVBBC4KOPPgLw+/Nabjdt2jQ4Ojqqn22zdu1azJ8/X+NZO3K5HBYWFtDT00NDQ8MdH3B5K8/Dpm15/qigoADW1taSbJtaJyAgAEIIHD9+XOooREREWo1FRgL+/v7Q1dXF4cOH233bS5cuxcyZM1t876effgIADBw48K7jCCFQVFSEY8eOtfj+b7/9BgDw8/MDADQ1NSExMRG2trZYvHgxrKysIJPJAAA1NTUtjqGnp4eXXnoJRUVFWLt2Lb7//nssXry42XqTJ09GY2MjEhMTm733wQcfoFevXmhsbLzrPj0o2pbndnFxcXjsscck2Ta1To8ePeDs7Kz+N0REREQtY5GRgIWFBUJDQ/Hll19Ksv2tW7finXfeQU5ODurq6pCTk4Nly5Zh8+bN8Pf317h0626mTp2KrVu3Ij8/Xz3WmjVr8M4778Df3x9PP/00AEBXVxcjR45EYWEhVq9ejZKSEtTU1CAuLg7r1q274/gLFiyAubk5/vGPfyA8PBwODg7N1lm5ciXc3Nwwd+5c/PTTT1AqlSgrK8OXX36Jd955B2vWrNG4p+Zh07Y8t8THx+P8+fMd/mGlXcHAgQORkpIidQwiIiLt1v4TDJAQQsTFxQkAIjo6ul23q1Qqxfr168WYMWOEs7OzMDAwEKampsLf31+sXLlSVFdXt2qcpqYmkZCQIJYuXSoee+wxYW9vL/T09IRCoRADBgwQK1asaDYFc3FxsVi4cKFwdHQU+vr6wsbGRsyZM0e8/vrr6tnOWprR67XXXhMARGpq6h3zlJaWildeeUW4uroKfX19YWVlJUJDQ0VsbKzGeiYmJs2mW7axsbnjuFFRUc3WnzFjhli9enWz5cuXL29znvbS1NQkBg0aJEaNGiXJ9qltPvjgA2Frayt1DCIiIm12WCbEH25MoHYzY8YM/PLLLzh16hRsbW2ljkOd2BtvvIHVq1cjJSUF/fr1kzoO3UVcXBxGjx6N3NzcFs9CEhEREeJ5aZmEvvjiC5ibm2Ps2LEoLy+XOg51Uhs2bMB7772Hjz/+mCWmg/D394eOjg4vLyMiIvoTLDISMjMzw4EDB1BSUoJhw4bh0qVLUkeiTuaDDz7A/Pnz8c9//hMLFiyQOg61kpmZGdzd3XnDPxER0Z9gkZGYs7Mzjh07BkNDQwwaNAi//PKL1JGoE6irq8OcOXOwfPlyrFixAm+//bbUkaiNeMM/ERHRn2OR0QIODg6Ij4/HqFGj8Pjjj+Of//wnamtrpY5FHdTx48fx2GOP4ccff8RPP/2EZcuWSR2J7sHAgQN5RoaIiOhPsMhoCWNjY+zYsQNr167Fxx9/DG9vbxw6dEjqWNSBVFRUYNGiRQgICIClpSVSUlIQEhIidSy6R76+vigrK8PVq1eljkJERKSVWGS0iEwmw6JFi3Du3Dl4eXkhJCQEU6ZMQVpamtTRSIvV1tbiP//5D/r27Yvt27fjm2++wcGDB+Hm5iZ1NLoP3t7ekMlk/PdPRER0BywyWqhnz56IiorCjz/+iOzsbPj6+uKJJ55Aamqq1NFIi9TU1OCTTz6Bm5sbli1bhmnTpuH8+fN4+umnIZPJpI5H98nCwgK9evVikSEiIroDFhktNn78eJw8eRK7du3C5cuX4efnh7Fjx2L37t1obGyUOh5J5MqVK/jHP/4BV1dXvP7665g6dSouXbqEjz76CJaWllLHowfI29sbZ86ckToGERGRVmKR0XIymQzh4eE4ceIE9uzZAyEEnnjiCTg7O+Ott95Cbm6u1BGpHTQ1NWHv3r0YN24cXF1dsWHDBjzzzDPqAsMHqnZOLDJERER3JhNCCKlDUNvk5uZiy5Yt+PTTT5Gfn4+AgABEREQgIiIC9vb2UsejB0SlUiEpKQk7d+7Ezp07UVhYiKCgICxYsADh4eHQ19eXOiI9ZNu3b8fMmTNRWVkJQ0NDqeMQERFpk3gWmQ6svr4e+/fvx44dOxAdHY2bN28iMDAQERER+Mtf/gJXV1epI1Ib1dbW4ujRo4iKisKuXbtw/fp1+Pj4ICIiAtOnT+cN/F3M+fPn4enpiZMnT8LPz0/qOERERNqERaazqK2txYEDB7Bz505ER0ejoqICvXv3RmhoKEJCQjB69GiYmZlJHZNacObMGcTExCA2NhZHjhxBTU0NfH191WfZ3N3dpY5IEmlqaoJCocAXX3yB2bNnSx2HiIhIm7DIdEYNDQ1ISkpCTEwMYmJicPLkSejo6GDQoEEYMmQIhgwZgoCAAN5XIYHGxkacOnUKycnJSEpKwpEjR1BQUABLS0sEBwcjNDQUoaGh6Nmzp9RRSUsMGDAAI0eOxJo1a6SOQkREpE1YZLqCkpISHDx4EHFxcUhOTkZ6ejpUKhXc3NwQEBCAwYMHw9fXF97e3lAoFFLH7TSEELh06RJOnz6NEydOICkpCSkpKaiurka3bt0QEBCAoUOHIiQkBP7+/tDR4dwb1NzTTz+NkpIS7N+/X+ooRERE2oRFpiuqqKhAcnKy+nX8+HGUl5dDJpPBxcVFXWp8fHzg6ekJFxcX3lh+FyUlJcjIyMCZM2eQmpqK1NRUnD17FpWVldDV1UWfPn0wePBgDB06FAEBAejbty+f9UKt8v7772PDhg3Izs6WOgoREZE2YZGh3+Xk5CAtLQ1paWnqA/Hs7GyoVCro6enB2dkZ7u7u8PDwgLu7O9zd3eHk5IRevXrByMhI6vgPnRAChYWFyM3NRXZ2NrKyspCRkYGsrCxkZWXhxo0bAABzc3N4e3uri6CPjw/69esHY2NjifeAOqrIyEhMmzYNBg/trwAAIABJREFUN2/e5MxlRERE/8MiQ3d28+ZNZGZmqg/WMzMz1d+Xlpaq1+vevTscHBzQq1cvODg4wMHBAT179oSlpSV69Oih8ae2nYWoqalBSUkJSkpKUFRUhNLSUhQXF+PatWvIz8/HtWvXcO3aNRQUFKC+vh4AoK+vDxcXl2bF7la5I3qQzp49i/79++PMmTPo16+f1HGIiIi0BYsM3ZuysjJcvXoV165dQ25uLvLy8tQH/Xl5ecjPz0dV1f+zd+dxUdeJ/8BfAwzDzHArNyiggIocmkeGpqhpXuGapJaJne7aobtdln23NvuZbqbZZpvVdlhuXpvmQZqZqCBteIMIeKEcgsN9DTAw798fLpPTgAICnwFez8djHguf+RyvmfnYzov356gwWsbCwsJQatRqNRwdHaFSqaBUKuHg4AC1Wg2VSmU4T0elUkGhUBiWt7KyMjqHp66uDuXl5UbbKCkpgRACOp0OFRUVKCsrg1arRWVlJUpKSgw/FxYWoqCgAFVVVUbLW1lZoWfPnvD29jYpZ7179zZMs7Kyauu3lKhRNTU1UKvV2Lx5Mx588EGp4xAREZmLQ/w2Rq3i7OwMZ2dnhIeHNzlPTU2NoTAUFhbi+vXrhp8bikVVVRWqqqpw6dIl5OTkoKKiwnA+Tnl5Oerq6ozWd3PxkMlkcHR0NNqmnZ0drKysDKXHzs4OKpUKarUavXv3Nvx88yhRw88uLi4m6yOSmkKhQO/evZGWliZ1FCIiIrPCIkPtRqFQwNPTE56ens2af9q0adDr9dizZ087JyPqXIKCgpCeni51DCIiIrPC672SWSgpKcH+/fsxa9YsqaMQmR0WGSIiIlMsMmQWtm/fDgCIioqSOAmR+enXrx+LDBER0e+wyJBZ2LJlC+6//344ODhIHYXI7AQEBKC0tBTXr1+XOgoREZHZYJEhyRUXF+Pnn3/mYWVETfDz8wNw435PREREdAOLDEnu+++/h4WFBaZNmyZ1FCKz5OPjA7lcjkuXLkkdhYiIyGywyJDkdu7ciXHjxsHW1lbqKERmycrKCt7e3rh8+bLUUYiIiMwGiwxJqqamBj/99BNHY4huw8/Pj0WGiIjoJiwyJKkDBw6goqICU6ZMkToKkVnz9/dnkSEiIroJiwxJateuXRg8eDC8vb2ljkJk1jgiQ0REZIxFhiQjhMDu3bt5WBlRM/j5+eHKlSuoq6uTOgoREZFZYJEhyaSkpCA7O5uHlRE1g7+/P+rq6pCTkyN1FCIiIrPAIkOSOXDgABwdHTFo0CCpoxCZPV9fXwC8lwwREVEDFhmSzM8//4yxY8fC0tJS6ihEZs/V1RUKhQLZ2dlSRyEiIjILLDIkibq6Ohw+fBjjxo2TOgpRpyCTyeDu7s5Dy4iIiP6HRYYkcezYMZSWlmLs2LFSRyHqNLy9vVlkiIiI/odFhiRx4MABeHp6ol+/flJHIeo0vLy8eGgZERHR/7DIkCSOHDmCyMhIqWMQdSockSEiIvoNiwx1OCEEjh07huHDh0sdhahT4YgMERHRb1hkqMNdunQJhYWFGDp0qNRRiDoVLy8v5OXl8aaYREREYJEhCSQlJUEulyMsLEzqKESdipeXF+rr65Gfny91FCIiIsmxyFCHS0pKwsCBA6FUKqWOQtSpeHl5AQByc3MlTkJERCQ9FhnqcElJSRgyZIjUMYg6HTc3NwDgiAwRERFYZEgCZ86cweDBg6WOQdTpqFQqqNVqXL9+XeooREREkmORoQ517do1lJaWGt0/5ty5c5g1axbc3d1hZWUFmUwGmUwGR0dHwzyrVq0yTPf29pYi+h3ZsWOHIb9MJkN1dbXUkSTXnM+0O+wbLeXq6oq4uDjuT+1g06ZNhvfUxsam0Xk2b96M8PBwKJVKw7wpKSkdnJSIiAAWGepgGRkZAICgoCAAQGZmJkaMGIFz587hu+++Q1lZGcrKyrBlyxZYWPy2e7744osQQnTaCwRMnz4dQghERUVJHcVs3O4z7S77Rku5urrCxcWF+1M7mD17NoQQGDduXKPPJyQkYM6cOZgwYQI0Gg0uXLjQLcozEZG5YpGhDpWeng47Ozu4u7sDAD755BOUlpZi3bp1uOeee6BSqWBnZ4fo6GgUFRVJnLblbG1tMXLkSKljdAldbd9oK66urjy0TCJbt26FEAKLFi2Cra0t+vTpg6ysLAwcOFDqaERE3RKLDHWo8+fPIzAwEDKZzPA7AISGhkoZq9MZNGgQVq5ciatXr0odpd1w32icm5tbtysy5rK/Z2VlAQB69OghaQ4iIrqBRYY6VHp6uuGwMgDQ6XQAAIVCIVWkTsnDwwNLly6Fr68vxowZg08//RQlJSVSx2pT3Dca1x1HZMxlf6+vr+/wbRIRUdNYZKhDZWRkIDAw0HDy+/fffw8ARifO3vyYP39+s9ar0Wjw/PPPw9fXF9bW1nBxccGMGTNw6tQpwzy/Pyk8KSkJ48aNg52dHVQqFSIjI5GQkGC03rq6OmzevBn33Xcf3N3doVQqERISgrVr10Kv15usu7KyEgkJCYbtWFlZNZo3Ly8Ps2bNgqOjI3r06IGpU6fi4sWLzX4fY2NjkZ2djVWrVqGsrAxPP/003N3dMWPGDHz33Xeoqalp9roA04sRXLlyBbNmzYKdnR169OiBRx99FMXFxcjMzMS0adNgZ2cHDw8PPPXUUygvLzdZX2FhIf7yl7+gT58+sLa2hpOTEyZNmoSDBw82O0tH7hudiYuLi0mRae7+1JzPxZz2hQZtvb83SEtLw/Tp0+Hg4AC1Wo1Ro0YhPj7eZL6m9sl+/fqZ7Jdvv/02gBv/7bh5+syZM1uVkYiIbkEQdRCdTifkcrn497//bZgWFRUlAAitVms0r0ajEQBETEyM0fSwsDDh5eVlNC03N1f07t1buLm5iT179ojy8nKRkpIiRo8eLWxsbMTRo0dN1qFWq8WIESPE0aNHRUVFhUhKShKhoaHC2tpaxMXFGebdtWuXACCWL18uioqKhEajER988IGwsLAQL774oslrVKvVIiIiosn3oOH1RkVFGbZ94MABYW9vL4YOHXrb97Apqamp4tVXXxW9evUSAISjo6N48sknxcGDB4Ver2/2ehryzZgxQxw7dkxUVFSIDRs2CABi0qRJIioqSpw8eVKUl5eLjz/+WAAQf/7zn43Wce3aNeHn5yfc3NzErl27RGlpqUhPTxczZswQMplMfPrpp0bzN/aZ3pylI/eNzmDjxo1CLpcLIVq2P7X0c5FiX2iuttjfz58/LxwdHYWXl5f48ccfRXl5uThz5oyYMGGC8PX1FQqFwmSZpvbJiRMnCgsLC3HhwgWTZUaMGCE2btzYqtdJRES3FMciQx0mKytLABAJCQmGaW3xZTUmJkYAMPmycO3aNaFQKMRdd91lsg4A4uTJk0bTz5w5IwCIsLAww7Rdu3aJMWPGmLyWuXPnCrlcLkpLS42mN7fI7Nq1y2j6ww8/LAAIjUbT5LLNodfrRVxcnHjyySeFo6OjACBCQ0ObvXxDvj179hhNDw4OFgDEoUOHjKb7+fmJoKAgo2nz588XAMS3335rNL26ulp4enoKpVIp8vLyDNPbs8i0dN/oDGJjYwUAUV5e3qL9qaWfixT7Qkvdyf4eHR0tAIht27YZTc/JyREKhaJFRWbfvn0CgFi4cKHR9Pj4eOHl5SVqa2tb+MqIiKgZ4nhoGXWY3NxcAICnp2ebrnfHjh2wsLDA1KlTjaa7u7sjODgYx48fR3Z2ttFzarUa4eHhRtNCQkLg6emJ06dP49q1awCAqVOnNnoITFhYGHQ6Hc6ePduqzEOHDjX63cvLC8Bv71FrNdz/wsbGBnK5vNXrGTJkiNHvDZ/Z76d7eXmZZN6+fTsAYMqUKUbTFQoFxo0bB61Wi3379rU6W0u0Zt8wd05OTgCA4uJiw7Tm7E+t/VzMeV+4k/197969AICJEycaTff09ERgYGCL1jVhwgSEhITgyy+/RGFhoWH6u+++i+eee+6O/i0SEVHTWGSow+Tm5kImkxkuvdwWampqUFpaCr1eDwcHB5Pj1U+cOAHgtytgNbj5hoo3c3V1BQDDOQilpaX461//ipCQEDg5ORnW+9JLLwEAqqqqWpXbwcHB6PeG+6LcfN5NS2RkZOCNN95A3759cffdd+Obb77BAw88gJ9//rlV54LY29ub5LO0tIRKpTKabmlpaZS54fOwsbGBnZ2dyXrd3NwA3Dino721dt8wd40VmdvtT3fyuZjjvnCn+3tNTQ3Ky8thY2MDW1tbk+cb/jvQEosXL0ZVVRU++ugjQ8aff/4ZTz/9dIvXRUREzcMiQx0mNzcXPXr0aPKO2a2hUCjg6OgIKysr6HQ6CCEafURGRhotV1hYCCGEyfoaCkzDF5lp06Zh2bJleOqpp5CRkQG9Xg8hBNasWQMAJutouKx0R8jPz8cHH3yAYcOGISgoCCtWrEBISAi2bduGvLw8fPbZZ4iMjOzQTAqFAg4ODqiurm70xO/8/HwAaNMye6ssrdk3zF1jReZ2pPhc2nqbbbm/KxQK2NnZobq6GhUVFSbPt+Y+RY888gjc3Nzw4YcfoqamBu+99x5iYmIMnxcREbU9FhnqMLm5uW1+WBkAzJgxA3V1dSZXHAOAlStXolevXqirqzOaXl1djaSkJKNpycnJyM3NRVhYGDw8PFBfX4+EhAS4u7vj+eefh4uLi+FLklarbTSLSqVCbW2t4fegoCB88sknd/oSTUyZMgVeXl5YvHgxlEol1q9fj7y8PGzfvh0PPvigpJcs/sMf/gAA2LNnj9H0mpoaHDhwAEql0uRwnvbSmn3D3DWMDLakyADSfC5ttc322N8nTZoE4LdDzBoUFBQgPT29xetTKBRYuHAhrl+/jvfeew8bN27EokWLWrweIiJqPhYZ6jDtVWTeeecd9OnTB48//jh++OEHlJaWoqioCOvXr8dbb72FVatWmVwG2cHBAa+99hoSExNRWVmJY8eOYe7cubC2tsbatWsB3DhUZsyYMcjLy8O7776LgoICaLVaHDx4EB9//HGjWQYPHoyMjAxkZWUhMTERly5dwqhRo9r8Nefm5uLtt99GZmYmDh06hKefftps/vL7zjvvwM/PD4sXL8bu3btRXl6OjIwMPPzww7h27RrWrl1rOKyoI7K0dN8wd3K5HGq1usVFRorPpa222R77+/Lly+Hs7IzFixdj//79qKioQGpqKubOndvo4WbNsXDhQiiVSrz++usYP348+vbte0cZiYjoNiS4wgB1UxMnThSPP/64EEKI7du3CwBGj0ceecQw3++fa7ha0M2PpUuXGtZdWFgo/vKXvwh/f38hl8uFi4uLmDBhgti/f79JjoarW6WmpoqJEycKOzs7oVQqxejRo0V8fLzRvBqNRixYsED4+PgIuVwu3NzcxPz588WSJUsMOW6+8lVaWpoYNWqUUKvVwsfHR6xbt04IIURiYmKT+X8/fcqUKW37xjdDU/mSkpJMpr/zzjviyJEjJtPfeOMNw/oKCgrE4sWLhZ+fn5DL5cLBwUFMnDhRHDhwwDDPu+++2+g2pdw3Ogs3N7dW7U/N+Vyk2Bekkp6eLqZPny7s7e2FUqkUQ4cOFbt37xbjxo0zvJYnnnii0X0SgEhMTDRZ51NPPdXoVd2IiKjNxcmEaOREAaJ2MGjQIEyaNAnLly+XNEd4eDgKCgo63dWqiBqEhYXhgQcewLJly6SOQr/zxRdfYN26dTh27JjUUYiIurpDPLSMOkxRURGcnZ2ljkHU6Tk6OqKkpETqGNSIjz/+GH/5y1+kjkFE1C2wyFCHKSkpMZvzOIg6Mzs7u0avtkUd77PPPsMf/vAHVFRU4OOPP0ZxcTEeeughqWMREXULLDLUIerr61FeXi5pkVm1ahVkMhlOnz6NnJwcyGQyvP7665LlIWotW1vbRi9pTNLYsWMHnJyc8M9//hObNm3qdBeQICLqrHiODHWIwsJC9OzZEwcOHMDYsWOljkPUqT355JPIzs42uXQwERFRN8JzZKhjNFwqloeWEd05W1tbHlpGRETdHosMdYiGE5NZZIjunJ2dHQ8tIyKibo9FhjpEQ5FxcHCQOAlR56dWqzkiQ0RE3R6LDHWIhi9ddnZ2Eich6vx4sj8RERGLDHUQrVYLS0tLXs2HqA3w8stEREQsMtRBqquroVQqpY5B1CXY2tpCq9Wivr5e6ihERESSYZGhDqHVallkiNqIQqEAANTU1EichIiISDosMtQhWGSI2o6NjQ0AFhkiIureWGSoQ2i1WsOXLyK6Mw0jMtXV1RInISIikg6LDHUIjsgQtZ2GPwqwyBARUXfGIkNtbseOHZDJZEaP5cuX4/Tp0ybTX3rpJanjEnU6PEeGiIiIRYbawaRJk5p9v5g5c+a0cxqirocjMkRERCwy1A4UCgUeeughyOXyW87n7++PwYMHd1Aqoq6DIzJEREQsMtROHn74Yeh0uiafl8vliImJ6cBERF0HR2SIiIhYZKidjBkzBi4uLk0+r9PpMHv27A5MRNR1NIzI1NbWSpyEiIhIOiwy1C4sLCzw6KOPNnp4mUwmQ2hoKAIDAyVIRtT5WVpaAgDq6+slTkJERCQdFhlqN3PmzGn08DIrKyvMmzdPgkREXUNDkamrq5M4CRERkXRYZKjdDBkyBH5+fibT6+rqMHPmTAkSEXUNVlZWADgiQ0RE3RuLDLWrefPmGR1eZmFhgbvvvhu9e/eWMBVR58YRGSIiIhYZamdz5841OrxMJpPxsDKiO2RpaQmZTMYRGSIi6tZYZKhd9e3bF2FhYZDJZIZpM2bMkDARUddgYWHBIkNERN0aiwy1u3nz5sHS0hKWlpYYN24cXF1dpY5E1OlZWVnx0DIiIurWWGSo3c2ZMwd6vR719fV49NFHpY5D1CVYWlpyRIaIiLo1K6kDkPmqr69HWVkZAKCiogI6nQ46nQ4VFRWGeSorK295U76ysjLU19cjKCgI58+fh16vx9atWw3PW1tbQ61WN7m8Uqk03MUcABwcHGBhYQGFQgGVSgUAcHJyavVrJOqsLCwsIISQOgYREZFkWGQ6Ob1ej+LiYsOjtLQUZWVl0Gq1qKysRElJCbRaLbRaLYqLi1FVVQWtVovS0lJUVlaiqqoK5eXl0Ov1KC0tBXD7cnInYmJi2mW9wG+lRyaTwdHREQDg6OgIpVIJlUoFR0dHqFQqKJVKODg4wNbWFkqlEnZ2drCzs4NKpYKtrS2cnJzg5OQER0dHliQyW3V1dYarlxEREXVHLDJmpKioCNevX4dGo4FGo0FeXh6KiopQUlJiKCo3/1xcXGwYMfm9hhGLhi/ySqUSTk5Ohp/9/f2NvtQDN770y2SyRguBSqWCQqGApaUl7O3tDduRy+WwtbVt8jXZ2NhAqVSiuLgYR48exZQpU4yeb+6IToPi4mIAQFVVFWpqahotYHV1dSgvL4cQAiUlJUblTaPRQKvVoqysDBUVFdBqtSgvL0d5eXmT5xs0FJuby83Nv/fs2ROurq5wcXGBi4sL3N3dYWdn1+RrImoLer0eFhY8OpiIiLovFpl2VlVVhStXriA3Nxc5OTnIyckxKSsFBQXQaDRGlykGgJ49e8LZ2dnoi3O/fv1u+cXawcHBcPiVOXFycjIpMQCgVqtveWhZR46I6HQ6lJeXN1kab56emZmJkydPori4GBqNxqRQ2tjYoGfPnnBzc4Obmxt69uwJFxcXeHh4wN3dHb169YKnpye8vLyMDp0jaq76+nqOyBARUbfGInMHqqurcfnyZWRmZiInJwfZ2dnIzs5Gbm4url69ipycHJSUlBjmt7GxgaenJ9zc3ODi4gJvb28MHTrU8CXX1dXV8JyLi4vh7t3UMeRyOZydneHs7NziZWtqaqDRaHD9+nXk5+dDo9GgoKAAeXl5uH79OgoKCnDu3Dnk5eUhLy/PqLS6urrC09MT3t7e8PHxgaenJ3x8fODt7Q1fX1/06tXL6KaiRMCNERkWGSIi6s74Tfk2iouLcenSpUYfmZmZ0Ov1AH4rKR4eHvD09MTUqVMNP/v7+xv+Em9uIyXUNhQKBby9veHt7d2s+YuLi5Gbm4tr167h0qVLhp/Pnz+PQ4cOITs722iUx8nJCf7+/o0+/Pz8jO7TQ12fXq+HEIJFhoiIujUWGQC1tbVIS0tDWloaUlNTkZqairS0NFy4cAFarRbAjS+qvr6+8Pf3R1BQECZNmmT4Iunr62t03gjR7TQcChgcHNzkPIWFhbh8+bJJgT527BiysrIM5/TY29sjICAA/fv3x4ABA9C/f38EBwfDz8+Po3pdVMN5Y/zDCBERdWfd6ltOXV0dUlNTcebMGaSmpuLcuXM4e/YsLl++jLq6OlhZWcHf3x8DBgzA1KlTERAQYCgrXl5e/NJAHapHjx7o0aMHhgwZYvKcTqfD1atXDeXm/PnzOHv2LD755BNcuXIFQghYW1sjKCjIqOCEh4ejb9++3Jc7uYaRYI7IEBFRdyYTXfRGBDqdDhkZGTh+/LjhcfLkSVRVVUEul8PHxwcDBgxAcHCw4X/79+9vuDcJUWdVW1uL8+fPIzU1FWfPnkVqaiouXbqElJQU1NTUwNbWFmFhYbjrrrsM+/+wYcNgbW0tdXRqpqqqKqjVauzevbvRi2gQERF1A4e6TJHJyMhAfHw8EhIScOLECZw9exY6nQ62trYIDw/H4MGDDY/+/fvzkBvqdqqrq5GcnIwTJ04YHsnJyaipqYFKpUJoaCiGDBmCiIgIjBo1Cl5eXlJHpiaUl5fD3t4esbGxmDRpktRxiIiIpNA5i0x9fT2Sk5Nx+PBhxMfH48iRI8jLy4NSqcSwYcMwdOhQQ2kJCAjgYTRETdDpdDh79qyh2CQlJeHEiROoq6uDv78/Ro0ahVGjRmHkyJEICgqSOi79T0lJCZycnLBv3z5MmDBB6jhERERS6DxF5sqVK4iNjUVsbCyOHDmC0tJSODo6YuTIkYYvWkOGDOHhMUR3qLKyEomJiYiPj8fhw4fx3//+F1VVVXBzc0NkZCQmT56M+++/Hy4uLlJH7baKiorQo0cP/PTTTxg3bpzUcYiIiKRgvkWmrq4OR48eRWxsLPbs2YOUlBTY2trivvvuw9ixY3Hvvfdi4MCBHG0hamc6nQ7Hjh1DfHw89u/fj8OHD0On02HYsGGYOnUqJk+ejPDwcF4CugNpNBq4urri4MGDGDNmjNRxiIiIpGBeRUav1yMuLg5fffUVdu3aheLiYgQEBGDKlCmYMmUK7r33Xo64EEmsoqIC+/fvN4yQ5ubmwsvLCw8++CBiYmIwePBgqSN2eXl5efDw8MChQ4dw7733Sh2HiIhICuZRZDIyMvD1119jw4YNuHr1KoYNG4bZs2cbLoFMROZJCIGTJ09i9+7d+Pe//4309HQMHDgQMTExeOSRR+Dh4SF1xC6poTzGx8cjIiJC6jhERERSkK7I6HQ6bNmyBR999BGOHj0Kb29vzJ07F/PmzUP//v2liEREdygxMREbNmzA5s2bUVZWhokTJ+K5557DxIkTeehZG8rKykKvXr2QmJiIu+++W+o4REREUjjU4SeYVFdXY82aNfD398f8+fPh4+ODffv24cqVK3jnnXfMpsQ88MADkMlkePvtt5u9jK2tLWQyWaMPGxsbhIaGYt26dfh9d2xsOQsLCzg5OSEsLAwLFy7E8ePHm73NVatWGZ7v16+f0XMjR45s3RvSjNf4+8dnn312R9uizmfEiBH45z//iWvXruHbb79FXV0dJk+ejNDQUGzcuNFwI0e6M/X19QB4Q0wiIureOqzICCHw+eefIyAgAK+//jqio6Nx4cIFbNq0CRMmTDCrk/Y3bNiAXbt2tXi5iooKnDx5EgAQFRUFIQSEEKipqcEvv/wCe3t7PPvss3jllVduu5xOp0NaWhreeustpKWlYciQIXjsscdQVVV122VffPFFw/MHDx5EeHg45s+fD51Oh/j4eMNyAQEBmDp1apu8xt8/Ro8ebbRMa7bVHXTV90ahUCA6Ohr79u3DqVOnMGjQIMTExCA8PBx79+6VOl6nV1dXB4BFhoiIurcOaQ9paWm49957sWDBAjzwwAO4cOECVq9ejd69e3fE5lskNzcXixcvxqOPPtpm67S2tkZ4eDi+/fZbWFhYYM2aNSgqKrrlMpaWlnBzc0NUVBR+/vlnvPzyy/jyyy8xZ84ckxGdpqSlpeGee+7B1KlT8cUXXxjdBFQIAb1e3+hfyG1tbe945OZmt9pWd9eW701bf25tJTQ0FBs2bEBycjICAgIwadIkzJo1C/n5+VJH67Qa/qChUqkkTkJERCSddi8ymzZtwtChQ1FTU4OkpCSsW7fOrE8AfuqppxAdHd0uN5nz8fGBh4cH6urqcPr06RYtu2LFCgwfPhw7d+7Epk2bbjt/QkICRo8ejVdffRXLli0zed7Ozg4XL15EbGxsi3I0V1xcHJ588skO2VZn1p3em/79++M///kPfvjhB/z6668YPHiwYYSQWqayshIAoFarJU5CREQknXYtMmvWrMGcOXPw8MMPIz4+HuHh4e25uTv2+eef4+zZs0bnmLS1htEUGxubFi0nk8nw7LPPAgA++uijW8773XffISoqCv/617/w9NNPty5oKz377LNYvHhxh26TOpf7778fp0+fxj333INx48Zhx44dUkfqdDgiQ0RE1I5F5ptvvsELL7yANWvWYP369WZ//5fs7Gy88MIL+Pzzz2FnZ9cu27h69SquXbsGe3t7BAcHt3j5hsOGfvnlF+h0ukbn+fDDD7Fw4ULExsY2ed7Fjh07jE7Kr66uBgCsWrUKMpkMlZWVSEhIMDx/8yFpLdXUtn4/PTMzE7NmzYKjoyN69OiBqVOn4uLFiybrS0tLw/Tp0+Hg4ACVSoVhw4Zh9+7dGD9+vGFdDSNBwI2c6d4JAAAgAElEQVQbBz7//PPw9fWFtbU1XFxcMGPGDJw6darJjOnp6XjooYfQo0cPw7QlS5YYfvb29kZSUhLGjRsHOzs7qFQqREZGIiEhoUXr/Oyzz9rkvWmPz6292dvbY8uWLZg/fz5mzZqFX375RepInQqLDBEREQDRDrKzs4WdnZ148cUX22P17WLixIli4cKFht+//vprAUAsW7asRes5efKkACCioqIM02pra8XJkydFRESEsLa2Fhs2bGjWcr+n1WoFAAFA5Obmmixra2srAIgXXnihWVmjoqIEAKHVao2mq9VqERERcdvX2NRj0aJFzd5Ww/SoqChx9OhRUVFRIfbv3y+USqUYOnSo0bznz58Xjo6OwsvLS/z444+ivLxcpKSkiPHjxwsXFxehUCiM5s/NzRW9e/cWbm5uYs+ePYb5R48eLWxsbMTRo0cbzTJ69Ghx8OBBUVlZKX755RdhaWkpNBqNEEKIsLAwoVarxYgRIwx5k5KSRGhoqLC2thZxcXEtXmdbvDfN+dzMkV6vF5MnTxZ9+vQRNTU1UsfpNDZt2iQsLCyEXq+XOgoREZFU4tplROaf//wn7OzsWnTpYil9+umnOH/+PP7+97+32Tq///57w1/Gra2tMWjQILi6uiI1NbXVFxIQtznJ38vLC/b29njvvffa9fC4Bo1dteyZZ55p1bqefPJJjBgxAmq1GuPHj8eUKVOQlJSEgoICwzyvvfYaSkpKsHbtWtx3332wtbVFcHAw/v3vfxvOGbjZq6++iitXrmD16tWYPHmyYf5NmzZBCIHnnnuu0SyvvPIKxowZA5VKheHDh6Ourg49e/Y0PF9ZWYmPPvrIkHfIkCH45ptvUFtbi0WLFrVqnXf63nRWMpkM69evx9WrV7Ft2zap43QaVVVVUKlUvDcPERF1a+1SZA4ePIiZM2dCoVC0x+rb1NWrV/HSSy/h888/b9MTZ2/+kp+dnY1Zs2Zh+/bt+OSTT1q9zmvXrgEA5HJ5o1+C+/Xrh71798LOzg4vvfQSVq9e3eptdbShQ4ca/e7j4wPgxlXkGjRctnfixIlG87q4uKBfv34m69yxYwcsLCxMDrFzd3dHcHAwjh8/juzsbJPlhg0bdsusarXa5HyvkJAQeHp64vTp04bPqSXrvJXmvDedmbe3N0aPHo0DBw5IHaXTqKys5GFlRETU7bVLkSkoKICrq2t7rLrN7dq1C6WlpRgzZozReQkNoyb/93//Z5h24cKFVm3Dy8sLX375Jfr06YN3330Xx44da9V6Gq7wNGLECMjl8kbnGTFiBH744QfY2trihRdewPvvv9+qbbXWhx9+2KptOjg4GP3ecE5Vw2WJa2pqUF5eDhsbG9ja2pos7+TkZPR7TU0NSktLodfr4eDgYHKzzhMnTgAAzp8/b7Ku2xVaR0fHRqc37PPXr19v8Tpv5XbvTVfg5ubWJUaYOkrDiAwREVF31i5Fxt/fH2fOnGmPVbe5Z555ptEbOn799dcAgGXLlhmm9e3bt9XbsbGxwfLlyyGEwJIlS1q8vF6vx7p16wyZbyUiIgKxsbFQq9X485//jH/84x8t2pY5Hq6iUChgZ2eH6upqVFRUmDz/+/KgUCjg6OgIKysr6HS6Jm/cGRkZ2eIshYWFjR7m15BBqhJvjp9bc50+ffqO/n11NywyRERE7VRk5syZgx07djR61anuLDo6GoMGDcKBAwewf//+Fi376quv4tdff8Uf/vAHREdH33b+UaNGYc+ePVCpVHj++ecNJag5VCoVamtrDb8HBQXd0SFxbWXSpEkAYHJn+Ly8PGRkZJjMP2PGDNTV1RldTazBypUr0atXL8Md0luiuroaSUlJRtOSk5ORm5uLsLAwye6TZK6f2+3s27cPKSkpmD17ttRROg2tVst7yBARUbfXLkVm9uzZCA4OxsMPP2y4TCjd+It5wwUQlixZcsuT9/V6Pa5fv47vv/8e48aNw9///nc8/vjj2LhxY7P/8j569Gjs3r0bSqUSzz777G3vP9Ng8ODByMjIQFZWFhITE3Hp0iWMGjWqWcu2p+XLl8PZ2RmLFy/G/v37UVFRgZSUFDz22GNwd3c3mf+dd95Bnz598Pjjj+OHH35AaWkpioqKsH79erz11ltYtWpVqy5R7ODggNdeew2JiYmorKzEsWPHMHfuXFhbW2Pt2rVt8VJbxVw/t1vJzc3FE088gejoaJNzgahpPEeGiIgI7XP5ZSGESEtLEy4uLmLMmDGipKSkvTbT5hYsWNDoJYUnTpx422XVarXJcrNmzTKZb+TIkYbnIyIiGl1OJpMJBwcHERISIv70pz+J48ePN3ub7777rtE8P/30k1AqlYbn77rrLpNlHnnkEcP8aWlpYtSoUUKtVgsfHx+xbt26W27Pzc2tyfdk+/btjW4rMTHRZPrSpUuFEMJk+pQpUwzrS09PF9OnTxf29vZCpVKJe+65Rxw6dEiMGTNGqFQqk+0XFhaKv/zlL8Lf31/I5XLh4uIiJkyYIPbv32+Yp7EsTf3TCAsLE15eXiI1NVVMnDhR2NnZCaVSKUaPHi3i4+NbtM62fm9u9bmZoytXroigoCDRv39/UVRUJHWcTuWxxx4TkyZNkjoGERGRlOJkQtzmmr53IDk5GRMnToStrS22bt2KsLCw9toUdXP9+vWDVqvFlStX2nU74eHhKCgoaPRqZ9R8e/fuxaOPPgoPDw/8+OOPjY6oUdNmz56N+vp6bN26VeooREREUjnULoeWNQgJCcGJEyfg6emJoUOH4rXXXuOhZtRqeXl5cHZ2hk6nM5qemZmJixcvYuzYsRIlo+bSaDSIiYnB5MmTMWnSJCQmJrLEtAJP9iciImqnc2Ru5u7ujoMHD+Kzzz7D+vXr4evri5UrV6Kmpqa9N01dUHFxMRYsWICsrCxUVVXh119/xaxZs2Bvb4//+7//kzoeNaGyshIrV65EQEAAfvrpJ2zZsgUbNmzgCeutxCJDRETUAUUGuHGS+7x583Du3DnMmTMHb7zxBgIDA7F69WqUl5d3RATqAtzd3fHTTz+hpKQE9957L5ycnPDAAw8gICAAv/76K/z9/dtt26tWrYJMJsPp06eRk5MDmUyG119/vd2211Xk5eVh6dKl8PHxwYoVK/Dyyy8jIyMDM2fOlDpap1ZZWckSSERE3V67niPTlKtXr2LNmjX417/+BZlMhpkzZyImJgajRo3q1PfCICJAp9Nh7969+Oqrr7B79244Ojriueeew8KFC01uXEqtM3DgQMycORNvvvmm1FGIiIik0r7nyDSlV69eWLNmDa5evYq33noLp06dwujRo9GnTx+88cYbvP8MUSd04sQJLF68GF5eXpg+fbrhUteZmZlYunQpS0wbKioqgrOzs9QxiIiIJCXJiExjUlJS8NVXX2Hjxo3Iy8vD0KFDMWXKFEyZMgWDBw/mSA2Rmamrq0N8fDxiY2Oxa9cupKWlITAwEPPmzcOjjz6KXr16SR2xy1IqlVi/fj3mzZsndRQiIiKpHDKbItOgvr4e+/fvx/bt2xEbG4vs7Gx4eHhg8uTJmDx5Mu677z7Y2dlJHZOoW9JoNPjhhx+wZ88e/PjjjygpKUFQUBCmTp2KBx98ECNGjJA6YpdXU1MDGxsb7Ny5E9OmTZM6DhERkVTMr8j83qlTpxAbG4vdu3fj119/haWlJYYNG4ZRo0Zh5MiRiIiIgIODg9Qxibqk/Px8xMfH48iRIzhy5AhOnToFuVyO0aNHG0ZM+/TpI3XMbiUvLw8eHh44cuQIRo4cKXUcIiIiqZh/kblZQUEB9u3bh7i4OMTHxyMtLQ2WlpYICQnBqFGjDOXGw8ND6qhEndLFixcRHx+Pw4cPIyEhAenp6bC0tERYWBhGjhyJyMhIjB8/Hra2tlJH7bbOnTuHAQMGICUlBcHBwVLHISIikkrnKjK/d/36dfz3v/9FQkIC4uPjkZSUhNraWnh4eOCuu+5CcHAwBgwYYPiZiH6Tm5uL48ePGx6//vorrl+/DisrK4SFhSEiIgIjR47E+PHjeaK+GTl69CgiIiKQk5MDT09PqeMQERFJpXMXmd8rLy/Hf//7Xxw/fhwnTpzAiRMncPHiRQgh4ObmhsGDB2Pw4MEIDw/HgAEDEBAQALlcLnVsonal1WqRlpaG1NRUnDx50vBvo7S0FFZWVujXr5/h38aQIUMwZMgQKBQKqWNTE/bs2YOpU6eisrKSN8UkIqLurGsVmcaUlpYafXk7ceIEMjIyUF9fD7lcjj59+mDAgAHo168fgoOD0a9fP/Tr149fEKjTKSkpQVpaGs6ePWsoLmlpacjMzIRer4dcLsfAgQMNpWXw4MEICwuDUqmUOjq1wDfffIMnnngCNTU1UkchIiKSUtcvMo2prq5GWlqa4cveuXPncO7cOZw/fx61tbWQyWTw9fVF37594e/vjz59+sDf39/w4MUFSCoajQaXLl0yeWRkZCA3NxcAoFKpDIW8oZwPGDAAffr04QhkF/CPf/wD/+///T/k5eVJHYWIiEhKh6ykTiAFGxsbhIeHIzw83Gh6XV0dLl68aPiL9oULF5CWlobY2Fjk5ORAr9cDAHr06GFUbHx9feHl5QUfHx94enqiZ8+eUrws6uSEEMjPz0dOTg5ycnKQlZWFy5cvGxWW8vJyAIBcLkevXr3g7++PoKAgTJkyBQMGDED//v3Ru3dvWFhIcq9b6gDFxcU8Z4mIiAhAtywyTbGyskJQUBCCgoJMnqupqUFmZqbhC2XDF8zY2FhkZmaitLTUMK+NjQ28vb3h6emJXr16wdPT01B0PDw84ObmBldXV6jV6o58eSShsrIy5OXlQaPRIDs7G7m5ubh69Spyc3MNpSUvLw+1tbWGZVxcXNC7d2/4+/tj0qRJRuXZx8cHVlb859sdlZSUsMgQERGBRabZFApFkyUHACorK02+mObm5iIrKwupqanIyclBfn6+0TIqlQqurq5wd3eHi4sLXFxcjH52c3NDjx494OTkBCcnJx7SZiaEECguLkZJSQmKioqg0WgMj4ayotFokJ+fj/z8fGg0GqPzGSwtLeHm5mYYwbvrrrsQFRVlUnxtbGwkfJVkrjgiQ0REdAOLTBtRq9Xo378/+vfv3+Q8NTU1yM/Pb/LLbk5ODo4fP254TqfTGS0vk8ng5OQER0dHQ7m5+feG/1Wr1VCpVHBwcIBarYZSqYS9vT1sbW2hUqm69T1ASktLUVVVBa1Wi+LiYmi1Wmi1WpSUlKCqqgoVFRUoKSlBcXGxoazc/L/FxcVGo28NbGxsDOXT1dUVLi4uCA4ONhTTnj17wsPDwzAPR1OotVhkiIiIbuC3qQ6kUCjQq1cv9OrVq1nzFxUVobCwsNEv0zf/nJmZiZMnTxqmVVZWorq6+pbrtre3h1KphFqthoODAywsLKBSqaBQKGBhYWEY/VGr1bC2toaVlRXs7OwAAHZ2dkZfxOVy+S3LkaOjI2Qymcn0+vp6lJWVNblcaWmp4bwk4EYRrKqqAnDj8BohBKqrq6HVaiGEQElJCQCgqqoKNTU10Ol0qKioQFlZGbRaLSorK2/5nqjVaqjVapNy2K9fP5OyePPPLi4uhveGqL0VFxfD29tb6hhERESSY5ExY87OznB2dm7Vsg1f7CsrK6HValFWVoby8nJotVpUVFSgtLQUWq0WVVVVKC4uBgBUVFRAp9Ohrq7OcFL5tWvXUF9fj9raWkMR+H3BaCgON6utrTWccN6wrsY0lKjGNBSrBjcXJnt7e1haWsLa2hpqtdpwpTkLCwvY2NhAqVTC0tLSMBJlY2OD9957DxcuXMDjjz+OJ554As7OzlCpVIbRK6LOoKSkBI6OjlLHICIikhyLTBfVcBiaVIeg+Pv7IyYmBm+88YYk22/M448/jg8++ACvvPIKTp8+ja+++goeHh5SxyJqkYKCAl4ZkYiICACv0UptLjc3F5cvX8bIkSOljmJEJpNh0aJFOH78OGpraxEeHo61a9eiG95KiTopIQQ0Gg3c3d2ljkJERCQ5Fhlqc0eOHIGVlRWGDx8udZRGBQcH45dffsHLL7+MF154Affffz+ys7OljkV0WwUFBdDpdHBzc5M6ChERkeRYZKjNxcfHY9CgQWZ9dTS5XI4333wTCQkJyMzMxMCBA/HJJ59IHYvolhou4c4iQ0RExCJD7SA+Pt7sDitryvDhw3Hq1Cn88Y9/xJ/+9Cc89NBDKCwslDoWUaNYZIiIiH7DIkNtqqysDMnJyZ2myACAUqnEihUrsHfvXiQmJiI4OBg7d+6UOhaRifz8fFhZWfE+MkRERGCRoTZ29OhR1NfX45577pE6Sovdd999SElJQVRUFKKiojBv3jxUVFRIHYvIID8/H25ubk1espyIiKg74f8bUpuKj49HYGBgp72qkoODA9avX4+tW7fihx9+QEhICA4dOiR1LCIAvxUZIiIiYpGhNtaZzo+5lZkzZyIlJQVhYWGIjIzEokWLTG76SdTRWGSIiIh+wyJDbUan0yEpKalLFBngxgnVO3bswJdffokvvvgCd911F3799VepY1E3lpeXxyJDRET0Pywy1GaOHTuGqqqqLlNkGsybNw+nT5+Gm5sbIiIi8Nprr3F0hiTBERkiIqLfsMhQm4mPj4ebmxsCAgKkjtLm/Pz88NNPP2HdunX48MMPMXjwYI7OUIdjkSEiIvoNiwy1ma5yfkxTZDIZnn76aSQnJ8PT0xMjRozAggULUFVVJXU06gaEENBoNCwyRERE/8MiQ21CCIHExMQuXWQa9O7dGz/++CO++OILbN26FaGhoTh8+LDUsaiLKywshE6nY5EhIiL6HxYZahPnzp2DRqPpFkUGuDE6M2/ePJw9exYDBgxAZGQkFixYgMrKSqmjUReVn58PAJ320uZERERtjUWG2kR8fDzUajXCw8OljtKhPDw8sHPnTmzatAnbtm1DWFgY7ztD7SIvLw8AOCJDRET0Pywy1Cbi4+MxYsQIWFlZSR1FEtHR0UhJSUFwcDBHZ6hdXL16FSqVCj179pQ6ChERkVlgkaE20dVP9G8ODw8PfP/999i8eTO2bduG0NBQxMXFSR2LuoisrCx4e3tLHYOIiMhssMjQHcvNzcXly5e7fZFpEB0djbNnzyIkJARjx47FggULUFFRIXUs6uSys7Ph4+MjdQwiIiKzwSJDd+zIkSOwsrLC8OHDpY5iNtzd3bFjxw5s3rwZ//nPfxAWFoaDBw9KHYs6MY7IEBERGWORoTsWHx+PQYMGwdbWVuooZic6OhqnT59G//79MX78eCxatIijM9QqHJEhIiIyxiJDd4znx9yal5cXdu/ejS+++AIbN25EcHAwdu/eLXUs6mSysrJYZIiIiG7CIkN3pKysDMnJySwyzTBv3jxkZGTg/vvvx7Rp0zBt2jTk5ORIHYs6gfLycpSWlvLQMiIiopuwyNAdOXr0KOrr63HPPfdIHaVTcHZ2xvr163Hw4EGkp6dj4MCBWLt2LfR6vdTRyIxlZ2cDAEdkiIiIbsIiQ3ckPj4egYGBTd5tfNOmTZDJZJDJZLCxsWl0ns2bNyM8PBxKpdIwb0pKSnvGbpb2zD5mzBicPn0aCxYswAsvvIDRo0fj3Llzbf0SWo2fm3nJysoCwCJDRER0MxYZuiO3Oz9m9uzZEEJg3LhxjT6fkJCAOXPmYMKECdBoNLhw4YLZHD7T3tmVSiVWrFiB48ePo6amBmFhYViyZAlqamra6iW0Gj8385KVlQW1Wg1HR0epoxAREZmN7nkbdmoTOp0OSUlJiImJafU6tm7dCiEEFi1aBFtbW9ja2hr++mzu2ip7WFgYjh49inXr1uH111/Hd999h/Xr1yMyMrIdUrcNfm4di1csIyIiMsURGWq1Y8eOoaqq6o5O9G/4AtmjR49mLzNo0CCsXLkSV69ebfV220JrsjfFysoKixYtwpkzZ+Dv749x48Zh3rx5KCoquuN1twd+bh2LVywjIiIyxSJDrRYfHw83NzcEBAS0eh319fUtXsbDwwNLly6Fr68vxowZg08//RQlJSWtztBarcl+O35+fti7dy82b96Mffv2YeDAgdi6dWubb+dO8XPrWByRISIiMsUiQ63W2PkxaWlpmD59OhwcHKBWqzFq1CjEx8ebLLtjxw7IZDJ8//33AGA46fruu+++7XZjY2ORnZ2NVatWoaysDE8//TTc3d0xY8YMfPfdd60+x6QjsjdXdHQ00tLSMG3aNMyaNQvTpk1r18OfuvvnNnLkSMNJ/zKZDHPnzgUAjB8/3mi6FMULuDEiY+7n8RAREXU4QdQKer1euLi4iDVr1himnT9/Xjg6OgovLy/x448/ivLycnHmzBkxYcIE4evrKxQKhcl6oqKiBACh1WpbnSU1NVW8+uqrolevXgKAcHR0FE8++aQ4ePCg0Ov1zVqHVNmb49ChQyIoKEg4ODiI999/X9TX17fp+vm53XDq1CmhVqtFWFiYqKioEEIIUV1dLYYPHy6+/fbbVr/OO6XX64VSqRRffvmlZBmIiIjMUByLDLXK2bNnBQCRlJRkmBYdHS0AiG3bthnNm5OTIxQKRbuXAb1eL+Li4sSTTz4pHB0dBQARGhrarGWlzn47VVVV4pVXXhFWVlYiIiJCnD17ts3WLfVrN6fPbcuWLQKAmDFjhtDr9SImJka89tprrXthbSQ7O1sAEIcPH5Y0BxERkZmJ46Fl1Crx8fFQq9UIDw83TNu7dy8AYOLEiUbzenp6IjAwsN0zNdw3xMbGBnK5vEXLSp39dm6+VHNdXR3Cw8OxaNEilJeX3/G6pX7t5vS5RUdHY+nSpfjuu+8wcuRIFBYWYtmyZS1eT1u6dOkSAMDf31/SHEREROaGRYZaJT4+HiNGjICV1Y0reNfU1KC8vBw2NjawtbU1md/V1bXdsmRkZOCNN95A3759cffdd+Obb77BAw88gJ9//hmnTp267fJSZm+p0NBQJCQkYPXq1diwYQMGDhyI7du3t3p9/NxMLVu2DMOHD8fRo0cRHR0NCwtp/zN56dIl2NjYwMPDQ9IcRERE5oZFhlrl9yf6KxQK2NnZobq6GhUVFSbzt/VlhPPz8/HBBx9g2LBhCAoKwooVKxASEoJt27YhLy8Pn332GSIjIyGTyW67ro7OfqcsLS3x7LPP4sKFC7j//vvx4IMPYvz48UhLS2vxuvi5mYqLi0NpaSlCQkKwcOFCnD59ulXraSuXL1+Gn5+f5IWKiIjI3PD/GanFcnNzcfnyZZMrlk2aNAnAb4f7NCgoKEB6enqbbX/KlCnw8vLC4sWLoVQqsX79euTl5WH79u148MEHoVAoWrzOjsrelnr06IH169cjLi4O+fn5GDRoEN58801UV1e3aD383H5z+fJlPPHEE/jPf/6DnTt3QqlUIioqChqNpsXraiuXLl3iYWVERESNYJGhFjty5AisrKwwfPhwo+nLly+Hs7MzFi9ejP3796OiogKpqamYO3duo4f+tFZubi7efvttZGZm4tChQ3j66afh5OR0R+vsqOzt4d5778XJkyexYsUKrF69GgMHDkRsbGyzl+fndkNFRQWmT5+O999/HwMGDICvry+2bduG3NxczJw5Ezqd7o6ythaLDBERUROkvtwAdT7PPvusGDp0aKPPpaeni+nTpwt7e3uhVCrF0KFDxe7du8W4ceMEAAFAPPHEE2L79u2G329+JCYmdvCr6RrZG1y9elXMmDFDABAPPvigyMzMbNZynfm1t0X2Z555xmhacnKy0Gg0JvMuW7asw1+fh4eHWL16dYdvl4iIyMzFyYQQon2rEnU1gwYNwtixY/Hee+9JHYWasHfvXjz//PPIycnByy+/jJdffhlKpVLqWNRCWq0WarUa27dvR1RUlNRxiIiIzMkhHlpGLVJWVobk5GRERERIHYVu4f7778fZs2exfPlyrF69GoGBgdiwYYPUsaiFLl26BCEEDy0jIiJqBIsMtcjRo0dRX1+Pe+65R+oodBtyuRyLFi1CWloaJk+ejMceewyRkZFITk6WOho1U8M9ZPz8/CROQkREZH5YZKhF4uPjERgYCHd3d6mjUDN5eHhg/fr1+OWXX1BdXY3BgwdjwYIFKCgokDoa3calS5fg5uZm9hecICIikgKLDLXI7+8fQ53H0KFDkZCQgI8++gjbt2/HgAED8Omnn6K+vl7qaNSEixcv8rAyIiKiJrDIULPpdDokJSWxyHRiFhYWeOqpp5CRkYGHH34YzzzzDAYPHoz9+/dLHY0akZ6ejqCgIKljEBERmSUWGWq2Y8eOoaqqikWmC3B0dMT777+PlJQUBAUFYcKECbjvvvtw5swZqaPRTdLT0xEYGCh1DCIiIrPEIkPNduTIEbi5uSEgIEDqKNRGAgMDsWXLFvz8888oLCzEoEGDMG/ePOTl5Ukdrdurrq5GVlYWR2SIiIiawCJDzZaQkMDRmC4qMjISx48fx6ZNm3D48GH07dsXb775JrRardTRuq3z589Dr9dzRIaIiKgJLDLULEIIJCYmssh0YTKZDNHR0UhNTcXLL7+MVatWoV+/fti4cSP0er3U8bqd9PR0WFhYoG/fvlJHISIiMkssMtQs586dg0ajYZHpBlQqFf76178iMzMT06dPR0xMDEJDQ7F161apo3UrGRkZ6N27N2xsbKSOQkREZJZYZKhZjhw5ArVajfDwcKmjUAfp2bMn1q5di+TkZAwYMAAPPfQQIiIiEB8fL3W0boEn+hMREd0aiww1S0JCAkaMGAErKyupo1AH69+/P7Zs2YLExETI5XKMGjUK9913H5KTk6WO1qVlZGTwRH8iIqJbYJGhZuGNMOnuu+9GXFwc9u/fD41Gg/DwcMybNw+5ublSR+uSMjIyOCJDRER0CywyZKS8vBzR0dFYu3Ytjh8/jrq6OuTm5uLy5cssMgQAGD9+PN1JOLQAACAASURBVI4fP45//etfOHToEAICAvDaa6+hpKRE6midUn19PTZs2IATJ04YrhKn0WhQVFTEERkiIqJbkAkhhNQhyLzY2NigtrYWQggolUqEhISgqKgIa9euxejRo6FWq6WOSGaitrYWX375Jf7617+iqqoKCxcuxJIlS+Do6Ch1tE7Fzc0N169fh4WFBby9veHn54cjR47ggw8+wOjRoxEUFAS5XC51TCIiInNyiEWGTPj4+CA7O9vwu0wmg5WVFXQ6HSwtLTFw4ECMHTsW06ZNQ2RkpIRJyVyUlpbi/fffx5o1a2BlZYUXX3wRzz77LGxtbaWO1ilERkYiLi7O8LuFhYXh35wQApaWlujTpw8mT56MNWvWSBeUiIjIfBzioWVkwsfHx+h3IQR0Oh2AG4fBnD59GmvWrEFBQYEU8cgMOTg44I033kBWVhZeeuklrFixAn5+fli5ciWqqqqkjmf2wsLCYG1tbfhdr9cbRkWBG//uMjIyMHDgQKkiEhERmR0WGTLh6+sLC4umdw25XI6JEyciOjq6A1NRZ2BnZ4dXXnkFFy9exDPPPIPly5fD19cXK1euNJz/QaYGDBiA+vr6Jp+3tLREv379MH/+/I4LRUREZOZYZMiEl5fXLS+zLJfL8emnn3ZgIupsevTogTfffBMXL17E448/jrfeestQaKqrq6WOZ3aCg4NvWWTq6+vx/vvvw9LSsgNTERERmTcWGTLh5eXV5HMWFhZ49913TQ4/I2pMz549sWLFCmRmZuKxxx7D3/72NwQGBmLt2rWoqam55bKFhYWIiYkxHNbYlQ0cOBAymazR5+RyOe69915MnDixg1MRERGZNxYZMuHl5dXol0crKyuEh4fjj3/8owSpqDNzcXExFJqHH34YS5YsQWBgID755BPU1dU1usx7772HDRs2YNasWU3O01U4ODigZ8+ejT5XV1eH999/v4MTERERmT8WGTLh7e2Npi5mt2HDhlueP0N0K66urlixYgUyMjIwffp0PP/88wgICDApNMXFxVi7di0AYOfOnd2izISGhppMk8vlmD9/PgYNGiRBIiIiIvPGb6RkorFDyywtLbF06VIEBwdLkIi6Gh8fH6xduxbp6emYMGECnnnmGcMITX19PT744APU1tYCuHF+yPfff4+HHnqoS5eZ0NBQoyuXATcuff63v/1NokRERETmjUWGTHh4eBgdr29paYnevXtjyZIlEqairqh3795Yv349zp49i4iICPzpT39CSEgIVq9ebVRa6uvrsXPnzi5dZn5/wr9cLseSJUt4PhoREVETWGTIhFwuh5OTk+F3vV6Pr776CjY2NhKmoq4sMDAQX3/9NVJSUuDs7IzKykqTeRrKzOzZs7tkmRk4cKBRkbGzs8MLL7wgYSIiIiLzxiJDjfL09ARwo9QsWLAAI0eOlDgRdQe9e/fGuXPnmrwUcX19PXbs2IE5c+Z0uTITHBxsGAm1sPj/7N13WBRX+zfwL0sTFliK9F4EpIgGFRSxAjYMlqAmavSxJpqov1gTS0zURKPGPImoUaNGYyxYgxWMWAC7FAHpvfeywOLCnvcP352HlUXpu8D5XNdewOzsnHvmzCxzz5xzhoUff/wRampqEo6KoiiKoqQXTWQosUxNTQEA6urq2LFjh4SjoXqK33//HWVlZe+cp76+HpcuXep2yYyKigr09PQAAJaWlpg/f76EI6IoiqIo6db0Uw+pLqu2thbV1dUoKytDdXU1eDweXr9+3ai5TmVlZaMTQVVVVcjJyTFXhr/44gskJSUBADQ0NKCkpARlZWVwOJzOWRmqx6itrcWOHTsgEAjeO68wmZk7dy5OnDjR6Q+KFB471dXVqK2tZY454M1wyZWVlWI/x+fzweVym1yugYEBcnNzMXfuXNy9e1fkPXV1dea4VFFRgby8PJSVlaGoqAhFRUUoKyu3z8pRFEVRVBchQ5oaZ5eSCnl5ecjOzkZubi6Ki4tRXFyMoqIiFBYWoqioiJlWWVkJLpeLioqKdz4hvD2x2WwoKytDVVUVmpqa6N27N7S0tJifWlpa0NbWho6ODgwMDGBsbEz72VBNOnDgAJYuXQoZGRnIy8sDADNyWVNkZWXxySef4Pjx4+8cFlx4nBQXF6O8vByVlZUoKysDl8sFl8tFZWUlysvLUVFRITKttLQUAoEA5eXlAICysrImhyaXBnJyclBVVYWMjAzU1dUhKysLNTU1qKurQ0VFhXmpq6tDTU2N+VtVVRXq6urM82x69+5Nm7VRFEVR0u4eTWQkSCAQIDMzE4mJiUhMTERGRgaysrKYn9nZ2SJPP1dSUmISBW1tbeaEQ0tLC6qqqlBRUYGamhqUlJTAZrPB4XCYOyjCE5qGlJSUGiUWwhO1gIAADBw4EFpaWqiqqgIhhLnDU1NTg/LycnC5XNTU1KCyspJJsBomW8KfDWlra8PQ0BDGxsYwNjaGkZERLC0t0adPH1hZWUFVVbXjNjgl1fLy8pCcnIyMjAxkZmYiMzMTqampSEtLQ1ZWFpNMAG8SGDk5Obx+/RqEEAwYMAAjR45ESUkJiouLmZ/Cl7ivOXEn98LjSHgscTgcsFgs5m6ImpoaZGVlwWazoaCggF69ekFJSQkKCgpgs9kiy2448p+QMMEQRyAQ4NChQ7CxsUH//v1F3nv7Lo/wggWXywWfz0dNTY3Indf6+npUVFQwd4BKS0uZBI3L5YpN2t4mJyfHXJDQ0tKCpqZmowsUenp6MDAwgI6ODnR1dd9fyRRFURTVfmgi0xl4PB6io6MRERGBhIQEJnFJSkpiEhUNDQ2YmpoyJ/gNT/YNDQ1haGjYJZuOCAQCFBQUIDs7G9nZ2cjIyEB2drZIwpaRkcE0cdPT04O1tTWT2Dg6OqJfv350CNoegsvlIj09HampqUhPT0deXh6ysrJQUFCAzMxM5s5kw68tWVlZsFgsaGpq4oMPPmCS+7dPvIXThAmLNKqtrYWioqJEyi4rK0NZWVmjixJvJ4XFxcUoLCxEQUEBampqmM/Ly8szd1/19PSgr68PfX19GBoawtTUlHnRu7IURVFUO6GJTHsrLi7G06dPERkZiYiICERFRSEhIQF1dXVQUVGBjY0Nc5Lep08fWFtbw8rKCr1795Z06BLD5/ORmpqKxMREJCQkICkpiUn20tLSAACampro378/nJyc0K9fP3zwwQewt7fv9L4RVNvU1tYiMTERKSkpSEtLQ1paGtLT05lXwzt4mpqaMDAwgKGhIXR1dZkTZOFdAF1dXRgaGorcCREIBO9sYka1r7KyMuTm5iIvLw85OTnIz89HdnY2c/EiPz8fGRkZIv2C9PX1YWZmxiQ2wt+trKxgbm4OOTnadZOiKIpqFprItFVKSgpCQkIQGhqKkJAQxMXFQSAQQF9fH/b29rCzs4OzszOcnZ1ha2tLT7xbqKKiAlFRUYiNjUVMTAyeP3+O8PBwVFdXQ0VFBU5OThg2bBjc3NwwdOhQaGlpSTpkCkBpaSliYmIQGxuLlJQUpKSkICYmBvHx8UwfLg0NDVhYWEBfXx8GBgawsLBgXpaWlk02waK6ntLSUuTk5CA3N5fZH4Qv4XTgzV0dY2NjZj+ws7ODvb09LCwsYG5uLra5HkVRFNVj0USmpZKSkhAYGIjAwEDcvXsX5eXlYLPZGDRoENzc3DBkyBC4urrSE+oOVF9fj5iYGISFhSEsLAwPHz5EUlISWCwW7Ozs4OXlBS8vLwwfPhxKSkqSDrdbKy0tZe48RkZGIjIyEnFxccwIXpqamrC2toaNjQ1sbGxgbW3N3IWkdUMJlZeXIykpCQkJCYiPj0d8fDwSEhKQkJDA3M1RV1eHnZ0d+vXrx9yddXR0FLkjR1EURfUoNJF5n5qaGgQFBeHmzZsIDAxEcnIyVFVVMXr0aIwZMwZubm7o168fbQ4hYQUFBXj48CHu3buHwMBAxMTEQElJCe7u7vDy8oK3tzdsbGwkHWaXlpmZicePH4skLhkZGQDeDOLg5OSE/v37o2/fvrC1tYW1tXWPbjJJtY/s7GwmsYmOjkZkZCSioqJQUVEBFosFS0tLJrFxcnKCi4sLtLW1JR02RVEU1fFoIiMOj8dDUFAQ/P39ceXKFXC5XAwYMAAeHh7w8PDA8OHDoaCgIOkwqXfIz8/H/fv3cfv2bVy9ehU5OTmws7ODr68vpk+fDjs7O0mHKNX4fD6ioqIQEhKC58+fIyQkBKmpqZCVlYWpqalIk0lh0x+K6kw5OTl4/vy5SLPThk17hU1OnZ2dMXjwYPqdTVEU1f3QREZIIBAgKCgIR48exbVr18Dj8TB8+HD4+vpiypQpzBO3qa5HIBDgwYMH8Pf3x8WLF5GbmwsHBwd8/PHHmD9/Pq1bAFVVVbh37x6CgoIQFhaG8PBw8Pl86OrqwtXVFa6urhgyZAgGDhxIm/JQUqu0tBSPHj3Co0eP8PDhQzx+/BgVFRVgs9kYOHAg3N3d4eHhgaFDhzLPKqIoiqK6LJrI5Ofn49ixYzh8+DBSU1MxfPhwfPzxx5gyZQp0dHQkHR7VzgQCAUJCQuDv74/Tp0+joqICPj4+WLJkCcaMGdNjOhMLBAI8f/4cQUFBTPLC5/Ph6OiI4cOHM4kLvdNCdWUCgQCxsbF49OgRwsLCcO/ePaSkpEBFRQUjRoyAp6cnvLy80LdvX0mHSlEURbVcz01kYmNjsX37dpw/fx5sNhtz587FkiVLYGtrK+nQqE7C4/Fw/vx5HDx4EKGhobCyssKaNWswb968btkMpaamBjdv3sT58+dx69YtFBcXQ19fH56envD09ISHhwe9O0V1e8nJyUwCf+fOHZSVlcHQ0BDe3t7w9fXFyJEj6eiSFEVRXUPPS2RiYmKwdetW+Pv7w87ODqtXr8b06dPpCEo9XHR0NPbt24djx45BT08PX3/9NebPn9/lE5qamhrcuHED/v7+uHr1KmpqauDu7o4PP/wQnp6ecHBwkHSIFCUx9fX1ePr0KQIDA3Hp0iVERERAW1sbU6ZMga+vL0aNGkWTGoqiKOnVcxKZvLw8rFq1CmfOnIG9vT02b96MqVOn0ofnUSIyMzOxY8cO/PHHH9DV1cWuXbswffp0SYfVYo8fP4afnx8uXrwIHo8Hd3d3+Pr6YurUqfSuC0U1ISkpCf7+/vD390d4eDh69+6NWbNmYenSpbC2tpZ0eBRFUZSo7p/IEEJw+PBhrFu3DhoaGti1axemTp3aY/pCUK2TlZWFLVu24OjRoxg/fjz8/PxgZmYm6bDeicfj4cyZM/Dz88OzZ8/wwQcfYMGCBZg2bRp0dXUlHR5FdSnJyck4e/Ysjhw5grS0NHh6emLZsmWYOHEivUtDURQlHe5169sRWVlZGD58OJYtW4aFCxciOjoa06ZNo0kM9V5GRkY4cuQI7t27h7S0NDg4OGD//v2SDkusyspKfPvttzAyMsKSJUtgbW2N0NBQPH/+HEuXLqVJDEW1gqWlJb755hskJSXhn3/+AYvFwpQpU2BlZQU/Pz/w+XxJh0hRFNXjddtE5uHDhxg0aBBKS0vx9OlT7Nq1C8rKyh1S1rBhwyAjIyP2tXLlymYvp76+HgcPHsTQoUPB4XAgLy8PAwMDTJgwAfv27UNaWlqHxN8WKioqTa77268jR440uZzdu3cz8xkZGXXiGrybu7s7wsPDsWrVKnz55ZdYvHgxXr9+LemwAAB1dXU4ePAg+vTpg19//RXLly9Heno6Tp06haFDh3Z6PHw+H3v37oWzszNUVVWho6OD8ePHIyAgAN38xi/VAtJ6rDeFxWLB29sbN27cQFxcHD788EOsWrUKDg4OuHTpkqTDoyiK6tG6ZSLz119/YdSoURg0aBAePnyI/v37SzqkZpkzZw6WLVuGyZMnIyYmBpWVlXjw4AEGDBiA5cuXY+DAgZIOsREul4vw8HAAgI+PDwghYl8jRowQ+UyfPn3g7e3NTFu9ejUIIXBycur0dXgfBQUFfPfdd7h06RLOnDkDDw8PlJSUSDSmmzdvwtHREStWrMAnn3yCpKQkbN68WWL9X6qqqjB69GgcP34ce/fuRUFBAZ49ewYVFRV8+OGHiImJkUhc7yJuP6Q63ruOdWmvkz59+uC///0v4uLi4OzsjGnTpsHd3R0RERGSDo2iKKpH6naJzN9//425c+di+fLluHz5MlRVVTul3KdPn4o9gf/ll1+a/fnTp09jwYIFWLt2LYyMjNCrVy9YWlpi+/bt+Pzzzzt4DToPIQQCgQACgUDSobTIhx9+iLCwMKSnp2P8+PHgcrmdHgOPx8MXX3yBCRMmwNHREa9evcLPP/8MLS2tTo+loTVr1iAqKgqBgYEYPnw4lJSUYGJiguPHj0NRUbHZy1FRUcGwYcM6MNL/6ez9sDPXTZJltsW76kSa1sXMzAx///03Hj9+DABwcXHB7t276Z1HiqKoTiYn6QDa07NnzzB//nz83//9H3766SdJh9MiwivWNjY2Yt+fPn06zp4925khtau7d+8yv6uqqiI5OVlywbSBg4MDbt++DXd3d8yfPx/nzp3rtLJLS0sxefJkREVF4fTp05gxY0anlf0u+fn5OHToEBYvXtyoPw6bzQaPx5NQZO/WlffD7qqr1cmgQYNw79497Nq1C9988w2ioqJw5MiRLj9sO0VRVFfRbe7IvH79GnPmzIG7u3uXS2IAMCeAQUFBYt8fMWIEioqKOjOkdvHFF1+0qJ9QV9CnTx+cPn0aFy9exMmTJzulTC6XCy8vL6SlpSE0NFRqkhgA+Oeff1BfXy81V8spqjOxWCysW7cOV69exZUrVzB79uwud7eZoiiqq+o2iczvv/+OjIwMHDlyRCLPhjl58iT69+8PNpsNDocDd3d3/P33383+vLu7O/T09HDr1i2MHz8ed+/efe8/w7q6Opw9exaenp7Q09ODkpISHB0d8d///pf5bFlZWaNO99u2bWM+33D6Rx99xCy7sLAQy5cvh5mZGRQUFKCtrY2pU6e2uS345cuXRcps7tX6joqntUaNGoXFixdj/fr1ndL5f8GCBcjMzMSdO3dgZ2fX4eW1xIsXLwAAGhoaWLVqFYyNjaGgoABTU1MsX768Wf2JhB3Aq6qqEBoayuwfcnJvbhq/vd/Ex8dj+vTp0NLSYqYVFRU165gQtzzhfvj29LS0NMyYMQPq6urQ0tKCt7d3i+8YvG/dhJq7j9fW1mLz5s2wtbWFsrIyNDU1MWnSJCahbEmZ79OSsoSd958+fYoxY8ZAVVUVysrKGDVqFEJDQ99bVlN10l7r0tG8vLwQEBCAgIAA5juWoiiK6mCkm7C2tiZLly6VSNlubm5kzpw55Pnz54TL5ZK4uDgyZ84cAoB8+eWXzV7OgwcPiLGxMQFAABAdHR0ya9Ys8vfff5OqqqpG8wcEBBAA5IcffiAlJSWksLCQ/Prrr4TFYpHVq1eLzDtu3DjCYrFIUlJSo+UMGTKE/P3338zfOTk5xNTUlOjq6pJr166RyspKEh0dTUaMGEF69epFwsLCRD4fHh7OxCzutWLFikZl+vj4EACkpqZGZLqTkxMxNDQUmdbSeDpLVlYWkZeXF9l2HeHWrVsEAAkMDOzQclpLWJd6enpk1qxZJDk5mZSWlpI///yTsNlsYm1tTcrKypq1LDabTdzc3N5b1ogRI0hwcDCpqqoijx49IrKysqSwsLBFx0TD5b29Hwqn+/j4kLCwMMLlcklQUBBRUlIigwYNatkGasa6tWQfX7hwIeFwOCQwMJBUV1eTvLw8snr1agKABAcHN7vM5mhJWU5OToTNZpMhQ4Yw2+zp06ekX79+REFBgdy9e7fR/G8f64Q0XSdtXZfO8ssvvxBFRUWSnJws6VAoiqK6u7vdIpFJSkoiAMiDBw8kHYqIwYMHEwDk0aNHzf4Mj8cjf/75J/Hx8SGqqqpMMqClpUVOnz4tMm9AQAAZOXJko2XMnj2byMvLk/Lycmba7du3CYBGyV5ISAgxMTEhfD6fmTZ37lwCgJw6dUpk3tzcXKKoqEicnZ1FpgsTGR8fn0axLFu2rM2JTEvj6UweHh5k9uzZHVqGj48P8fT07NAy2mLs2LEEADE3NxfZjwghZNu2bQQA2bRpU7OW1dxE5vr162Lfb8kx0XB5TSUyAQEBItM/+ugjAoAUFhY2a30aete6tWQfNzc3J0OHDm20DGtr63ZPZFpSlpOTEwFAwsPDRaZHRUURAMTJyanR/N0xkamrqyMmJibk66+/lnQoFEVR3d3dbtG0LDY2FgDg7Ows4UhECZtqBQQENPszioqK+PTTT3H58mWUlJTg33//xcyZM1FcXIzZs2czQx0DgLe3N4KDgxstw8nJCXw+X2TI2zFjxmDAgAE4fvw4iouLmem7du3CypUrRZppXL58mXl2QkN6enqwt7fH8+fPkZWV1ex1aitpi6chZ2dnZv/rKKGhofjwww87tIy2YLPZAAAPD49GzX0mTZoEALh161a7ljl48GCx01tyTDTHoEGDRP42NjYGAOTk5LRoOe/Tkn183LhxCAsLw+LFi/Ho0SOmiVd8fDxGjhzZrnG1tCw2m91ouHtHR0cYGBggMjISubm57RqfNJKVlYW3tzdCQkIkHQpFUVS31y0SmaqqKsjKyqJXr16SDkWEvr4+AKCgoKBVn5eTk8Po0aNx+vRprFu3DvX19Th//jzzfnl5OTZv3gxHR0doaGgwbcfXrFkDAKiurhZZ3qpVq1BdXc08oT4hIQH379/HwoULmXlqa2tRXl4OgUAADofTqH+NsD9EYmJis9Zh3759zR6CWpz2jqe9qaiodOgwzIQQlJWVoXfv3h1WRluZmZkBgNghoHV0dAC86f/RnoTJ09taeky8D4fDEflbOBpVe3bmbuk+7ufnhxMnTiAlJQVjxoyBmpoaxo0b1yEPZ2xpWerq6mKnC/eD1n4XdjW9e/eW+LOmKIqieoJukchoa2ujvr6+3U+W2kp41Vb4T/xdQkNDGw1d29CoUaMAvBmCV2jSpEnYunUrFi1ahISEBAgEAhBCsHfvXgBo9EyDGTNmwNjYGPv27UNtbS327NmDRYsWiTxrR1FREerq6pCTkwOfz2/yAZfCeDqatMXzttzc3GbVb2vJyMjA2NgYcXFxHVZGWwlHKxN3tV144vqufbshGRmZNsXS0mOiMzW1bi3dx2VkZDBnzhzcvn0bZWVluHz5MgghmDp1Kn7++edmldmSmJtbFgAUFxeL3cbC/aAtx0pb16UzvXr1iknwKYqiqI7TLRIZZ2dnyMrKijyrpLMcOXJEbJM2QgjzjBFh85p3IYSgoKAAjx49Evv+s2fPAAADBgwAANTX1yM0NBR6enpYvnw5tLW1mX/0NTU1YpchJyeHFStWoKCgAHv27MGZM2ewfPnyRvNNnToVdXV1Ykca2rlzJ0xMTFBXV/fedWov0hZPQ8HBwXBxcenQMnx8fHDy5MlOGR2tNSZMmABDQ0PcvHmz0Sh0wmaVkydPbtaylJWVRdbTxsYGhw4datZnW3NMdKZ3rVtL9nF1dXUmsZWXl4enpycz4te1a9eaXWZztKQs4M0DW58+fSoy7eXLl8jJyYGTkxNzl7o12rounSU/Px/Xrl2Dj4+PpEOhKIrq/jqnL07HGz9+PBk9enSnl3v48GGmE31iYiKpqakhcXFxZNasWS0atezBgwcEADE2NianTp0i2dnZhMfjkdTUVLJr1y6ioKBAnJ2dCY/HYz4zevRoAoD89NNPpLCwkFRXV5M7d+4QExMTAoAEBQU1KqeiooJwOBwiIyNDPv30U7Gx5OfnE0tLS2JhYUGuX79OysrKSHFxMTl48CBRVlYmZ8+eFZn/XZ39m9KSzv4tjaez3L17lwAgjx8/7tBy0tLSCJvNJhs3buzQctrixo0bRE5Ojvj4+JCEhARSWlpKTpw4QdhsNnFxcSHV1dXNWs64ceMIh8MhGRkZJCwsjMjJyZHY2Fjm/ab2G6GWHhPv6+z/9vR169aJ7dDe1nVryT7O4XDIiBEjSGRkJOHxeCQ/P59s2bKFACDbtm1r0fZ8n5aU5eTkRDgcDhkzZkyHjFrW1nXpDAKBgEybNo2Ym5uLHWmSoiiKalfdY9QyQggJDg4mAMjVq1c7tVwej0f8/f3JlClTiKWlJVFUVCQcDoeMHDmyRcPy1tfXk5CQELJ69Wri4uJCDAwMiJycHFFVVSUDBw4kP/zwQ6N/jIWFhWTJkiXE2NiYyMvLE11dXTJv3jyyfv16ZrQzcSN6rVmzhgAgkZGRTcZTXFxMvvrqK2JhYUHk5eWJtrY28fLyanQiyGazGw23rKur2+RyL1261Gj+WbNmkV27djWavmHDhhbH01nq6+vJ4MGDyahRozqlvN9//53IyMiQP//8s1PKa42wsDAyduxYwuFwiIKCArG1tSVbtmxpdhJDCCFxcXHE3d2dsNlsYmxsTPz8/AghhDx8+FDs0N5va+4x0dR+KK4c4X749vSJEye2aPs0tW5Czd3HIyIiyJIlS0jfvn2JsrIy0dTUJK6uruTw4cNEIBC0qMz3aUlZwsQkNjaWjB07lqiqqhIlJSUyYsQIEhISwszX1LHeVJ2017p0hnXr1hF5eXly584dSYdCURTVE9yVIUSCjcbb2axZs3Dnzh2Eh4dDT09P0uFQ3djmzZuxa9cuPH36FA4ODp1S5saNG/Hjjz9i586dWL16daeUSVHN1b9/fxQVFUlsBEFJ4vP5WLZsGY4ePYpjx45hzpw5kg6JoiiqJ7jXrRKZiooKDB48GEpKSggODm5yBB2KaoujR49i4cKFOHjwIBYvXtypZe/duxdr1qzBxIkTceTIEWhra3dq+RTVlJ6ayMTHx2P27NmIiIgAi8WClpYWDAwMoK+v3+inhoYGDAwMYGJi0miocoqiKKrFulciAwBpaWlwd3eHuro652NIEwAAIABJREFUrly5AgsLC0mHRHUjO3fuxDfffIONGzfiu+++k0gMjx49wuzZs1FcXIz169djxYoVUjf0ONXz9LREprS0FDt37sR///tfODg4YM2aNaisrEROTg4KCgqQnZ2N/Px85OXlITc3V2TACVlZWejo6EBXVxcGBgbQ0dGBoaEhdHR0oK+vD319fWZaU0ONUxRFUd0wkQGA7OxsTJ48GampqTh37hxGjx4t6ZCoLq62thZLlizBX3/9he3bt2PdunUSjaeqqgr79u3D9u3boa6ujo0bN2LhwoVgsbrFQIRdRnOGBP7222+xZcuWjg+mBdoz7t27dzPP6RHasGEDtm3b1trwpBqfz8exY8ewadMm1NfXY82aNfi///s/5hlDTamoqGCSnJycHOTn5yM3Nxd5eXnIz89HdnY2CgoKUFBQIDKENZvNhoGBAZP0GBkZwcTEBMbGxjAyMoKxsTH09PS61PDUFEVR7aR7JjLAmwffzZ07F1euXMG6deuwYcMGetWaapUnT55g8eLFyMjIwNmzZ+Hp6SnpkBjZ2dnYvHkzjh8/jr59+2LZsmWYM2cOVFRUJB0aRXUrBQUFOHLkCA4ePIiioiKsXLkS69evh5qaWruWU1dXh4KCAuZOjvDuTkFBAbKysphXXl4ek/AoKCjAyMgIRkZGMDU1FUlyzM3NYW5uDmVl5XaNk6IoSgp030QGePNsln379mHDhg3Q09PDgQMHMGbMGEmHRXURFRUV2LBhA/bv34+RI0fi0KFDsLS0lHRYYr18+RK//PILTp8+DXl5ecydOxdLly6Fra2tpEOjqC7t4cOH8PPzw/nz58FmszF//nwsX74cxsbGEo3r9evXKCoqQm5uLlJSUpCTk8P8Lvy7YbKjoaEBCwsLkZew/07fvn1pokNRVFfUvRMZoaysLHz55Ze4cuUKpk6dis2bN6Nfv36SDouSUjweD4cPH8aPP/6Iuro67N69G3PmzOkSTTdKSkpw9OhRHDx4ECkpKRg2bBh8fX0xbdo0GBgYSDo8iuoS4uPj4e/vj3PnzuHly5dwdnbGsmXLMHPmTCgpKUk6vGarqqpCWloaUlJSkJqaKvJKS0tDRUUFgDd9doyMjGBmZgZLS0v06dOHeVlZWdEkh6IoadUzEhmhq1evYtOmTYiMjMSUKVOwefNmODk5STosSkrU1NTg8OHD2LlzJ0pLS7FkyRJs3LgRWlpakg6txQQCAW7duoVTp04hICAAXC4Xbm5uNKmhqCbExcXh/Pnz8Pf3R1RUFHR1dTFt2jR8+umncHFxkXR4HaK4uLhRgpOcnIzExERkZGRAIBAAAIyNjWFlZSWS4FhbW8PCwgKKiooSXguKonqwnpXIAG+am125cgXff/89IiIiMHbsWCxZsgTe3t50OMweKj09HYcPH8Yff/yB8vJyLFmyBOvWres2zyLi8Xi4desWzp8/j3/++QdcLhcDBw6Ep6cnPD09MWTIkPd2VKao7obL5eLevXsICgpCYGAgXr16xSQvvr6+cHd3h6ysrKTDlJja2lokJSUhMTGx0Ss7OxvAmzs5JiYmsLOzg729Pfr27cv8pP30KIrqBD0vkREihODq1avw8/NDUFAQ9PX1sXDhQixcuBBGRkaSDo/qYPX19bh+/ToOHjyImzdvQldXF/Pnz8cXX3zRbRIYcXg8HoKCgnD9+nUEBQUhOTkZbDYbI0eOhIeHBzw9PWFvby/pMCmq3dXX1+PZs2cICgpCUFAQHj58iLq6Ojg6OsLT0xPe3t49PnlprqqqKpHEJjo6Gq9evUJcXBx4PB5kZGRgamoKW1tbODg4iCQ47T04AkVRPVrPTWQaysrKwqlTp7Bv3z7k5ORgyJAh8PX1ha+vL22C040IBAKEhYXB398f/v7+yMvLw5gxY7B48WJMnjwZ8vLykg6x06WkpOD27du4ffs2/v33X5SUlEBNTQ2DBw+Gm5sbnJ2dMWzYMGhoaEg6VIpqkYqKCjx58gQhISF4/vw5QkNDUVpaCh0dHYwYMQIeHh6YMGECvXDVjurr65GamoqYmBi8evUKMTExiI2NRVxcHKqrqwEAJiYmcHJyQv/+/ZmXubl5l+iDSFGU1KGJTEOvX7/G9evXce7cOVy9ehVVVVVMZ+kJEybQh2t2QTweDw8ePMClS5dw8eJF5Ofnw8nJCb6+vpg5c6bUjkImCfX19cwJ36NHjxAWFoasrCywWCwYGRnBxcUF3t7e6N+/P/r27dsjEz9KOtXU1CA6OhovXrzAw4cP8ejRIyQkJIAQAhsbG7i6umLIkCEYNmwYveMoAQKBAGlpaYiNjUV0dDTCw8MRGRmJxMRECAQCcDgc9OvXTyS5sbe3p/1vKIp6H5rINIXH4+HmzZvw9/fH1atXUVFRASsrK3h5ecHT0xOjR4+mt8il1MuXLxEYGIigoCDcv38fNTU16N+/P3OXrU+fPpIOUeqUlZXh1atXzNVT4RXV9PR0EEKYUY3y8vJQW1sLBQUF2NnZwcnJCf369YOTkxOcnJzQu3dvSa8K1c1lZWUhMjISUVFRiIiIQFRUFBITE1FfXw9VVVUMGjQIQ4cOhaurK1xdXbvkYB09RVVVFV6+fImIiAjm9fLlS1RXV0NeXh59+/bFBx98ABcXF7i4uMDR0ZH2ZaUoqiGayDQHn89HWFgYAgMDERgYiBcvXoDFYmHw4MEYOnQohg4diiFDhnTrvhXSqq6uDuHh4Xj48CHCwsJw//595ObmQktLCx4eHvDy8oKXlxdtPvL/lZaWIiUlhWnyIfyZmpoKQggUFBRgZWUFe3t7pgOvnZ0dbG1tISsrCz6fj/j4eJETycjISOTn5wMADA0NYWdnB2tra9ja2sLa2hrW1tYwMTEBi8WS8NpTXQWfz0dqairi4+MRHx+PhIQEJCQk4OXLlygpKQEAmJmZMUm08Gq+hYUF3c+6uPr6eiQmJjKJzdOnT/Hs2TNUVFRAWVkZH3zwAQYPHswkN6amppIOmaIoyaGJTGsUFRXh9u3bCA4OxsOHDxETEwOBQABLS0sMGTIErq6u6N+/P/r16wdVVVVJh9ttEEKQkpKCiIgIPH/+HGFhYXj69Cmqq6uhoaGBIUOGwM3NDZ6ennB2du7RJzSlpaWNkpWYmBjk5uYCANTU1NCnTx9YWFiIJCx9+/Zt0Xa7ceMGvv/+ezx69AiDBg3C5MmTkZyczJyAFhUVAQB69erFJDXW1tawsrKCqakp8xRyOmpaz1NTU4P09HTmlZiYiISEBMTFxSE1NRV8Ph/Am+TYxsYG1tbWIncB1dXVJbwGVGcRCAR49eoVnjx5gsePH+Px48eIjo5GXV0d9PT0mMRm6NChcHFx6VLP+qEoqk1oItMeKioq8PDhQ+b15MkTlJWVQUZGBubm5kxS4+TkBDs7O5ibm9P+Be9RVFSE+Ph4vHz5EpGRkYiMjER0dDQqKyshKyvLtHt3c3PDkCFDYGtr2+M6i9bW1jJXqoUv4chB5eXlAAAtLS1mtCBhomJnZwdDQ8M2lX3t2jV8//33ePLkCSZOnIjNmzdj8ODBjeYrKSlhTk4bxpmUlISamhoAAIvFgr6+PszMzGBmZgZTU1OYmJjA1NQURkZG0NPTo03WuhiBQICCggLk5+cjIyNDJGERvoR38QCAw+HAysoK1tbWsLGxYRIXa2trOowvJRafz0dUVBQzmMPz588RGxsLOTk5ODk5wcPDA25ubnB3d6dJL0V1XzSR6ShpaWmIiopCVFQUcyKenJwMgUAAOTk5mJmZMQ8VEz5gTHgC1xOuJhFCkJeXh6ysLOYBbPHx8cxwnqWlpQDAdAJt2A/DwcGhxzxpmhCCzMxMJgFo2NQmPT0dAoEALBYLJiYmzImfMGGxt7eHtrZ2u8bz4sULrFq1Cnfv3oWHhwe2b98uNoFpjoKCgkYnt2lpaUhLS0N6ejrz1HEAUFRUhK6uLgwNDaGjowMjIyPmb11dXejq6kJbWxuampr0LmgHKisrQ1FREYqLi5Gfn4+cnBzk5eUhNzeXeeXk5KCgoAB1dXXM51RVVWFtbc3chWuYtJqamtITTapd5ObmIiQkBCEhIQgNDWWagffv3x9ubm4YNmwYxowZA01NTUmHSlFU+6CJTGeqqqpCQkICc7IuPDlNTExEcXExM5+mpiYMDQ1hYmICQ0NDGBoawsjICFpaWujdu7fIT2m7C1FTU4OioiIUFRWhoKAAxcXFKCwsRGZmJnJycpCZmYnMzEzk5ubi9evXAAB5eXmYm5s3SuyEyV1PUF5ejqSkJKSkpIj0YYmPjweXywUAqKurw9LSEhYWFiJNwmxtbcFmszs0vuzsbGzYsAEnT56Ei4sLfv75Z7i6unZomaWlpcjJyRE5Sc7NzUVeXh6ys7NRUFCArKwsVFVViXxOQUEBWlpa0NTUFPkpPG44HA7U1dWhoqICFRUVqKqqgsPhQE1NDSoqKujVq1eHrpckVVVVgcvlgsvloqysDJWVlaisrASXy0VFRQVKSkpQXFyM4uJisb/X19eLLE9DQwP6+vrQ09ODgYGBSHJpZGQEFRUVHDhwAEePHsXIkSOxf/9+2NjYSGjtqZ4mNzcX9+/fx/3793Hv3j3ExsaCxWLByckJnp6eGDduHNzc3GgLCYrqumgiIy1KSkqQkZGBzMxMZGVlITs7mznpz87ORk5ODnNCK8RisZiTMzabDXV1dSgrK0NJSQkcDgdsNhvKysrMFWplZWWR4Szl5ORErl7X1dWhsrJSpIyysjIQQsDn85mTnZqaGlRVVaGsrIz5vbi4GEVFRcyzAhqW0bt3bxgZGTVKzkxNTZlpPWEkGmEdN+wPIGx2JWxmo6CgAEtLS5GmNcKmNu19d6U5qqur8dNPP2HXrl3Q1dXFjh074OvrK1UJdFVVFfLy8piT7oYn3g1PxoWv8vJyZr8WR3hcCJMdBQUFsNlsKCgooFevXlBSUoK8vDxUVFTAYrHA4XAAQORZOzIyMk3eZXj7OBSqrq5GbW1to+kCgYBpKgi86QwtvFslvHNZXl4OgUAALpcLPp+Pmpoa8Hg88Hg8Jll53zqrqamJTf7enia8iKKrq9vspO/Jkyf47LPPEBsbi7Vr12Ljxo20XxTV6QoLC/HgwQPcvXsXt27dQkJCAtTU1DBmzBiMGzcO48aNg4mJiaTDpCiq+Wgi05XU1tYiKysLubm5KCkpYe565Ofno7q6GhUVFeDxeKipqQGXy0V1dTWqq6uZBKi6ulrkiurr16/B4/GYv2VkZJjERniiymazISsrCzk5ObDZbLDZbCgpKUFZWRlqamrgcDhQVlaGpqYm8zI1NUXv3r2hra3d7ZqMCE8qq6qqUFNTg4qKCnC5XNTU1KCyshKlpaUoKChAUVERCgsLUVBQgNzcXKSnp4skokZGRiKd34UjfJmZmUnNk8UDAwPx2WefoaSkBN988w1WrFjRrZ7rUFVVxdyNKC8vR3l5OXO3QnjiX1lZyST4dXV1TLJRW1uL6upqJsF/O9kQThenoqKi0Z0NAEzCJI6amhqzXzRMkjgcDlgsFlRUVCAvL88kSYqKiszvKioqzB0nVVVV5i6Uuro683dnNGetr6/Hvn37sHHjRlhYWODEiRNwcnLq8HIpqimpqakICgrC7du3cevWLVRUVMDCwgIeHh7w9vaGl5dXt/rOo6huiCYy7a20tBTl5eWoqKhgTo6Er4qKCpSVlaGsrAzV1dXg8Xgid0GEV1WFV1PFvddVCU+0GhJe3W5IVlZW7PN5hCdsLS2jKcIT04bEXRGvqalBTU0NU2firpi/Haeuri6TyPXu3RuGhoYwNjaGiYkJ04ldmvtBlZaWYv369Th8+DAmTpyIAwcO0OGrqXaTlpaG//znPwgJCcGqVauwdetW2rSHkrja2lrcv38fN2/exM2bNxEbGws2m43x48fD19cXEydO7PAmvBRFtRhNZJry+vVr5op6Xl4eCgsLUVhYKPK7uIRFHHl5eebuhYaGBjgcDnNXo+HVVVVVVcjJyTFXUxs2WxG+J+7kv2GTFnGakwQ01JwTdgDM3Z+GxDVPAyC2WUtVVRXTT0ZIeKW7obevdovzrivg4igpKTVqFiPc7uKmaWhoQElJCUpKSu9twteV+fv7Y9myZZCTk8Nvv/2GadOmSTokqhsSCATYu3cvNm7cCCcnJ5w+fRrm5uaSDouiGOnp6bhx4wYuXLiA4OBgKCgoYMKECfjoo4/g7e1NR9OjKOnQcxOZ/Px8JCcnM31ShP1RhL/n5eWJnHj36tUL2traIiMkCZMTYbMNdXV1qKurM38L3+spI2xRXVdRUREWLFiAq1evYunSpfjhhx+6RWJGSbfY2Fh8/PHHyMjIwPHjx+Hj4yPpkCiqkZKSEly9ehX+/v64desWZGVl4eHhAV9fX0yePFlsKwKKojpF905k8vPzERMTg+TkZCQnJyMpKYn5Kbx6LycnBz09PZiYmDAd0hs+v0JbWxt6enr0i4rqtoKDgzFnzhzIysrir7/+gru7u6RDonoQHo+HdevW4bfffsOXX36J3bt306ZmlNQqLi7GpUuXcP78edy5cweysrKYOHEiFixYgLFjx/boBzFTlAR0j0SmoqICMTExePnyJaKjoxETE4OoqCjmqeIqKiqwsrKClZUVLC0tRX43MDCQms7VFNWZ6urqsG3bNmzbtg2TJk3CH3/8QZ+vQEnMiRMn8Pnnn+ODDz7AhQsXoKOjI+mQKOqdSkpKcPnyZfz555948OABjIyMMH/+fMyfP5+OfkZRnaPrJTKvX79GZGQknjx5gidPnuDp06eIj4+HQCCAiooK7Ozs0K9fP9jb28PBwQEODg7Q09OTdNgUJVWysrIwY8YMhIeH4+eff8Znn30m6ZAoCjExMZg8eTLq6+sREBAAe3t7SYdEUc2SkJCAo0eP4vjx4ygsLMTo0aOxePFi+Pj40KHGKarjSH8iU1lZifv37+POnTsIDQ1FREQEamtrweFwMGjQIAwePBiDBg1Cv379YG5uLlXPt6AoafTkyRNMnjwZ6urq8Pf3pyeLlFQpKSnB1KlT8eLFC5w+fRoTJ06UdEgU1Wx8Ph///PMP/vjjD9y6dQu9e/fG3LlzsXTpUpiZmUk6PIrqbqQvkamtrUVYWBju3LmDO3fu4MmTJ6ivr4eDgwNGjBjBJC42NjY0aaGoFvL398e8efMwbNgwnD17tts954fqHmpra7Fw4UKcOXMGBw4cwMKFCyUdEkW1WEZGBo4dO4Y//vgDOTk5mDp1KlatWgUXFxdJh0ZR3YV0JDLV1dX4999/4e/vj3/++Qfl5eWwsLCAm5sbhg0bhgkTJtDnWFBUGxBC8NNPP+Hrr7/GokWL4OfnBzk5OUmHRVFNIoTgu+++w/fff4/du3fjq6++knRIFNUqdXV1OH/+PPbs2YNnz57Bzc0N33zzDSZMmCDp0Ciqq5NcIlNSUoILFy7g0qVLuHPnDurr6zFixAhMmTIF3t7eMDU1lURYFNXtvH79GnPmzMHly5fh5+dHr25TXcqBAwewbNkyfPvtt/j2228lHQ5Ftcn9+/exa9cuXLt2DQMHDsTmzZsxceJE2sKEolqncxMZgUCAsLAwnDx5En/99RcEAgE8PDwwadIk+Pj4QFdXt7NCoagegcfjwdfXF/fv38eVK1cwcuRISYdEUS126NAhfP7551izZg127Ngh6XAoqs2eP3+O77//HgEBAXB2dsbOnTsxevRoSYdFUV1N5yQyubm5+OOPP3Ds2DGkpKTA1dUVCxYswIwZM+hD9yiqg1RXV2PKlCl48uQJbty4AVdXV0mHRFGtduzYMSxatAibNm2id2aobiM8PBwbNmzAjRs3MH78eOzcuROOjo6SDouiuoqOTWRSU1Pxyy+/4PDhw+jVqxd8fX2xdOlSODk5dVSRFEUBqKqqgo+PD168eIFbt25h0KBBkg6JotrsyJEjWLx4MQ4cOIAlS5ZIOhyKajf//vsv1q5di8jISCxevBg//PADHYyFot6vYxKZqKgo7NixA+fOnYOZmRnWrl2LuXPnQlFRsb2LoijqLVwuF+PGjUNSUhJu374NBwcHSYdEUe1m69at2LJlC86ePYuPPvpI0uFQVLsRCAQ4efIk1q5dCxkZGezZswezZs2SdFgUJc3usdpzaVlZWZg5cyb69++PmJgYnDx5EvHx8Vi8eDFNYiiqE9TV1WHGjBlITEzE3bt3aRJDdTubNm3CkiVLMGfOHDx48EDS4VBUu2GxWJg7dy7i4uIwZcoUfPrppxg3bhyys7MlHRpFSa12SWRev36NHTt2wNbWFs+ePcPFixcRERGBjz/+GLKysu1RBEVRzbBixQoEBwfj8uXLsLW1lXQ4FNUhfvvtN4wfPx7Tpk1DZmampMOhqHaloaGBAwcOIDQ0FKmpqejXrx/OnDkj6bAoSiq1OZG5ffs2HB0dsXXrVqxfvx7R0dGYPHkyHUqQojrZtm3b8Pvvv+Pvv//GkCFDJB0ORXUYWVlZnDx5Ejo6Ovjoo49QW1sr6ZAoqt25uroiPDwcM2fOxCeffIJ58+ahpqZG0mFRlFRpdSJTV1eH9evXw8vLCw4ODnj16hU2btyIXr16tWd8FEU1w+nTp7F582b88ssvmDx5sqTDoagOx2azcfHiRcTFxWHVqlWSDoeiOoSysjL8/Pxw9epVBAQEwM3NDWlpaZIOi6KkRqsSmaysLIwaNQq//vor9u7diwsXLsDExKS9Y6MoqhmePn2KefPmYe3atfjiiy8kHQ5FdRpra2scOXIE+/fvx99//y3pcCiqw0yYMAFPnz6FQCDAwIEDERISIumQKEoqtHjUssDAQMyaNQs6Ojrw9/eHnZ1dR8VGUdR7cLlcODs7w9DQELdv3waL1a7jd1BUl7By5UocP34cMTExMDQ0lHQ4FNVhqqurMWfOHNy4cQNnz57FpEmTJB0SRUlSy0YtO3HiBLy9vTFu3Dg8efKEJjEUJWGfffYZysrKcOrUKZrEUD3Wzp07YWBggMWLF0s6FIrqUMrKyjh37hzmzJmDqVOn4q+//pJ0SBQlUc0+89m/fz/mzZuH1atX48SJE2Cz2R0ZF0VR73HkyBGcPn0af/31F/T19SUdDkVJjKKiIo4cOYKbN2/SJmZUtycrK4vff/8dq1atwn/+8x9cvnxZ0iFRlMQ0q2nZ8ePHMX/+fGzfvh1ff/11Z8RFUdQ7xMbGYtCgQVi+fDl+/PFHSYdDUVJh2bJlOHv2LGJiYqCrqyvpcCiqwy1btgxHjx7F9evXMWrUKEmHQ1Gd7d57E5kHDx5gzJgxWLt2LbZt29ZZgVEU1QSBQIAhQ4aAxWLhwYMHkJOTk3RIFCUVKisrYW9vj3HjxuHQoUOSDoeiOpxAIMAnn3yC27dvIzw8HMbGxpIOiaI607v7yOTl5WHmzJmYMGECtm7dKnaeM2fOQEZGBjIyMk0OvXz27Fn0798fSkpKzLzR0dFtD59qF7QOu5aDBw/ixYsXOHjwoNgkhtZn10frsHVUVVWxbds2HD16FDExMU3OR7evdKP103wsFgvHjx+HkZERfH19wefz3zk/3bbSjdZPK5AmCAQCMmLECGJjY0PKy8ubmo0xZswYoqio2Gh6SEgIkZGRIWvWrCGVlZUkKSmJGBkZkZcvX753mdKssrKSWFlZkYkTJ0o6lHZD61D6FRQUEHV1dbJ27dr3zkvrs+ujddhy9fX1xNHRkXzyySfvnbenbd+uhtZP80VHRxNlZWWyefPmZs1Pt610o/XTbHebvCNz4cIFPHjwAKdOnYKamlqrEyV/f38QQrBixQqoqKjA0tISmZmZcHBwaPUym6KiooJhw4a1+3LFIYRAIBBAIBB0SnmSROtQevz8889QUFDA5s2bW70MWp9dH63DprFYLKxevRrnzp1DRkZGq5bRXbevJMtsT7R+GrO3t8fWrVuxe/duZGdnt7psum2lG62fxsQmMnw+H9988w1mz54NZ2fnNgWVmZkJANDS0mrTcqSNqqoqkpOTcf369Q4rY8CAAdi5c2er/xm3F1qH0qGkpAR+fn5YvXp1m0YNpPXZevSY7FjtVYczZ86ElpYWjh071qrPd9ft21Z0/5duX3zxBfT09PDdd9+1ehl024pH933pJTaRuXLlClJTU5vsF9MS9fX1bV5GT6Wvr48NGzbAzMwMI0eOxOHDh1FWVtbpcdA6lA6///47FBQU8Pnnn7dpObQ+W48ek12DgoICZsyYgVOnTrXq83T7ikf3f+mmoKCADRs24NixYyguLm7VMui2FY/u+1JMXIMzX19f4uHhIbYx2qtXr4iPjw9RU1MjysrKZNiwYeTBgweN2vNdunSJAGj0cnFxIW5ubiLTZs2aRQh50yaw4fTS0tJmNZDbtWuX2LJkZWXFxhIXF0d8fX2JpqYmM62wsJDw+Xxy5swZ4uHhQXR1dUmvXr2Ig4MD+eWXX0h9fX2T61ZTUyN2empqKpk+fTrhcDhEU1OTTJw4kSQlJTVrnYRyc3PJnj17yIABAwgAoqioSKZMmUIuXLhAeDxei5YlROuwc+uwvdja2pKVK1c2mt7W+rSxsWk0bevWrYQQQvh8vsj0adOmNSvW7lyf0nhM0jpsXIchISFMXF3xO0+ooKCAfPnll8TU1JTIy8uT3r17kylTppDw8HCR+Xg8Htm0aROxsbEhSkpKRENDg3h7e5MrV66Qurq6FpX5LtK4/9P6+Z/KykqipKRE/vjjj3bZtpL4bpHWbSuN+z6tH3K3USLD4/EIm80mBw8ebDR3YmIiUVdXJ4aGhiQwMJBUVlaSqKgo4uXlRczMzMR2TPLx8RH5pyQUERFB2Gw2cXJyIlwulynbxcWFnD59+n2Bi8Vms4mbm1uT7wtjGTFiBAkODiZVVVXk0aNHRFZWlhQWFpKAgAACgPzwww+kpKTxBkGsAAAgAElEQVSEFBYWkl9//ZWwWCyyevXqZq+bcLqPjw8JCwsjXC6XBAUFESUlJTJo0KBWrRshhMTGxpKvv/6amJiYEABEXV2dLFy4kAQHBxOBQNCsZdA6bN66dVQdtlZoaCgB0OgLoj3rc+zYsYTFYok9KRwyZAg5depUi+Pu7vUpbcckrcP/4fP5RFVVlXz33Xdd9jsvJyeHmJqaEl1dXXLt2jVSWVlJoqOjyYgRI0ivXr1IWFgYM+/ChQsJh8MhgYGBpLq6muTl5ZHVq1cTACQ4OLjZZbaEtO3/tH7e8PHxIZMmTerS3y3Sum2FpG3f78H10ziRSUpKIgDIixcvGs3t6+tLAJDz58+LTM/OziaKioot2uiEEHLu3DkCgEydOpUIBAIyd+5c8s0337RkBUQ09x/u9evXxb4fEBBARo4c2Wj67Nmziby8fKPR2973DzcgIEBk+kcffUSAN1cp20IgEJC7d++ShQsXEnV1dQKA9OvXr1mfpXUoHXXYUps2bSKWlpaNprdnfd66dYsAIEuXLhWZHhISQgwNDcnr169bHHdPqU9pOSZpHYry8PAgZmZmXfY7b+7cuQRAo5OQ3NxcoqioSJydnZlp5ubmZOjQoY2WYW1t3WEnc0LSsv8TQuuHEEK2b99OrKysuvR3i7Ru27dJy77fg+un8ahlWVlZAABDQ8O338LNmzcBAGPHjhWZbmBgAGtr60bzv4+vry82bNiAixcvYtiwYSguLm6XfjnvM3jwYLHTvb29ERwc3Gi6k5MT+Hz+O59LIM6gQYNE/hY+qConJ6dFy3mbcHzxXr16QV5evkWfpXUoHXXYUsHBwWKf2tye9enl5QVHR0ccP35cpH31rl278OWXX7Z4X2uJrl6f0nJM0joUZWdnx3SO7YrfeZcvXwaLxYK3t7fIdD09Pdjb2+P58+fM/+xx48YhLCwMixcvxqNHj5i29PHx8Rg5cmSHxCckLfs/QOsHAIyMjJCVldWlv1ukddu+TVr2/Z5cP40SmcrKSgBvhkBrqLa2FpWVlejVq1ej9wBAR0enVQFs3boVLi4uCAsLg6+vL1isdz6js100NeJTeXk5Nm/eDEdHR2hoaDAPGlqzZg0AoLq6ukXlcDgckb8VFBQAoNVDiyYkJODbb7+FlZUVXF1d8ddff+HDDz/EnTt3EBER8d7P0zqUfB22hkAgwLNnzxoNSdgR9bly5UpUV1dj//79AN7sc3fu3MHixYtbF3wzddX6lMZjktbh/xgaGqK+vr5LfufV1taivLwcAoEAHA6H2W7C14sXLwAAiYmJAAA/Pz+cOHECKSkpGDNmDNTU1DBu3DhcunSp3WMTksb9H6D1o66uDh6P12W/W6R52wpJ477fU+un0dFtYGAAoPHVLUVFRaiqqoLH44HL5TZaUElJSasCuHv3LsrLy+Ho6IilS5ciMjKyVcsB3mTGbTFp0iRs3boVixYtQkJCAgQCAQgh2Lt3LwCAENKm5bdGfn4+fv31VwwePBg2NjbYsWMHHB0dcf78eeTl5eHIkSMYNWpUs9ad1qFk6rCtsrOzwePxGl2h6Yj6nDVrFnR1dbFv3z7U1tZiz549mDt3LjQ0NFoVe3esT2k/Jmkd/o+Ojg5kZGS65HeeoqIi1NXVIScnBz6fD0KI2JfwTq2MjAzmzJmD27dvo6ysDJcvXwYhBFOnTsXPP//crDKbQ9r3f6Bn1w/w5n+GlpZWl/1ukdZtK+37fk+tn0aJjJGREQAgPT290czjx48H8L/bYUJFRUWIj49vceGpqalYsGABLly4gH/++QdKSkrw8fFBYWFhi5cFAMrKynj9+jXzt42NDQ4dOtSsz9bX1yM0NBR6enpYvnw5tLW1mQ1aU1PTqnjaauLEiTA0NMTKlSuhpKSE33//HXl5ebh06RKmTZsGRUXFFi+T1mHXk5aWBgAwMzNr9F5716eioiKWLl2KgoIC7NmzB6dOncKKFStavByh7lafXeGYpHX4PywWi7ka3xW/86ZOnYq6ujqEhoY2+tzOnTthYmKCuro6AG+uwsfFxQEA5OXl4enpicuXL0NGRgbXrl1rdpnv0hX2/55cP0KZmZkwMDDo0t8t0rZtu8K+32PrR1zPGVtbW7Jq1apG05OSkoimpqbICAsxMTFk7NixREdHp0UdkyorK0m/fv3IlStX/tdj5+5dIi8vT4YPH96qjknjxo0jHA6HZGRkkLCwMCInJ0diY2PfG4vQ6NGjCQDy008/kcLCQlJdXU3u3LnDjEoRFBTUrHVravq6devEjjzVlP79+5Mff/yRpKenN2v+5qB12Ll12B6EnfgqKioavdee9SlUWFhIlJSUiIyMDPHx8WlT7N2tPqX9mBSidfjGoUOHiJqaWpf9zsvPzyeWlpbEwsKCXL9+nZSVlZHi4mJy8OBBoqysTM6ePcssh8PhkBEjRpDIyEjC4/FIfn4+2bJlCwFAtm3b1uwy30Xa9/+eXj9CAwYMIJ9//nmX/m6Rtm0r7fu+UA+sn8ajlhFCyIYNG4ixsbHYIeTi4+PJ5MmTiZqaGjPs5dWrV0XGal+wYEGTY14/fPiQLFu2TGTay5cvSWFhYaN5hWNhN1dcXBxxd3cnbDabGBsbEz8/P0IIIQ8fPhQby9sKCwvJkiVLiLGxMZGXlye6urpk3rx5ZP369cxnnJ2dxa7brFmzxJazYcMGQghpNH3ixIktWrf2ROuwa9Xh9evXCQBmONG3tUd9vm3RokUEALl3716bYqf12Ty0DjumDr/77jtia2vbJb/zhIqLi8lXX31FLCwsiLy8PNHW1iZeXl6NEsCIiAiyZMkS0rdvX6KsrEw0NTWJq6srOXz4cKP/5e8rs7PR+mm/+klJSSEyMjLk9u3b7bZt39bR3y1C0rZtOwKtnzbXz10ZQho3UI6JiYGjoyMuXryIyZMnv/02RVGdKDQ0FMOGDUN2djbTh62jHTt2DH5+fnj27FmnlEe1P1qHwPz585GVlYXAwEBJh0JRneKrr77C6dOnkZGR0WGjFNLvFunWw+rnntihPOzt7TFz5kysW7cOfD6/s4OiKKoBYb814TCyneHgwYP46quvOq08qv3ROgSePn0KZ2dnSYdBUZ0iPT0d+/fvx6ZNmzp0qHX63SLdelr9NDkm4fbt25Geno7ffvutM+OhKOothoaGUFJSQmxsbIeVceTIEUyZMgVcLhcHDx5EaWkppk+f3mHlUe2P1qGowsJCvHr1Cq6urpIOhaI6xdq1a2FsbIxFixa163Lpd4t06+n102QiY25ujk2bNuHrr7/G48ePOzMmEW+PTy3utWXLFonFR70frcO2kZP7f+zdd1gUV/s38O/CAtJ7L4IgiCgIKPaCiGAUsWFBJf5i9zH2RImJ/bGhSewYjSYaYiMCwS6iKKKogBRBBBGl9w4Cy877hy/7iIDSh3J/rmsvYHZ25rvsLOw958w5XFhZWeHBgwetuh9vb2/Iy8vj2LFjOH/+PLhcbp3r0evZftFr+D9Xr14Fl8uFjY1Nm++7K/x+O7LO+PqcPn0anp6eOHz4cKu0xtDflvatK78+dV4jU43P52PChAl48eIFQkNDoaio2JbZCCH/35YtW/D777/j7du3bTLhKCEd3bBhw6Cqqop//vmH7SiEtKro6GhYWVlh+fLl2L17N9txCGlLAZ8tZAAgJycHlpaW0NDQwO3bt+udvZkQ0npiYmLQu3dv+Pv7CyaZIoTULSwsDBYWFrh37x5GjhzJdhxCWk1SUhJGjBgBTU1N3Lt3r94z8YR0Ul8uZAAgNjYWI0aMgJmZGXx9fZs08Q8hpHmsrKygr6+Pc+fOsR2FkHZt3rx5CAkJQURERLNnSSekvcrMzMSoUaPA5XJx9+5d6jVDuqK6Ry37lJGREW7cuIGnT5/CwcEBJSUlrR2MEPKJNWvW4NKlS02a8ZeQruLly5c4d+4c1qxZQ0UM6bSys7MxZswY8Pl83L59m4oY0mU1qEWmWlhYGOzs7GBoaIh///0XCgoKrZmNEPIRPp8PExMTmJqa4sKFC2zHIaRdGjNmDPLy8vDkyRMICwuzHYeQFhcbG4sJEyaAz+cjICBAMEQ/IV1Qw1pkqpmbmyMgIABJSUkYMGAAIiMjWysYIeQTQkJC+OWXX3Dx4kVcvXqV7TiEtDseHh64e/cujhw5QkUM6ZSqJ0hWVFREUFAQFTGky2v08EfGxsYIDQ2Frq4uhgwZAk9Pz9bIRQipg729PaZNm4Zvv/0W+fn5bMchpN1IT0/H2rVrsXDhQpo7hnRKx48fx+jRozF69GjcvXsXqqqqbEcihHVNGsdVUVERN2/exH/+8x9Mnz4dGzZsAJ/Pb+lshJA6HDlyBOXl5XBxcUEjeoYS0mlVVlZi+vTpkJGRwZ49e9iOQ0iLysnJwdSpU7Fs2TKsX78e58+fh7i4ONuxCGkXmjwhBZfLxe7du3Hy5En8+uuvsLOzw9u3b1syGyGkDioqKvDw8MC1a9ewd+9etuMQwrrvvvsOoaGhuHz5MmRlZdmOQ0iL8ff3R79+/RAUFISrV69i27ZtNIgFIR9p9sx633zzDR48eIDU1FT07dsXx48fp7PEhLSyUaNG4fDhw3B1dcXZs2fZjkMIa/78808cPHgQJ0+eRJ8+fdiOQ0iLKCgowLfffgtbW1sMGjQIL168gL29PduxCGl3WmSK8AEDBuD58+fYuHEjvv32W4wYMQJxcXEtsWlCSD2WLFmCNWvWYMGCBfD19WU7DiFtztPTEwsWLMAPP/yAmTNnsh2HkBbh4eGBXr164cKFCzh9+jQuXbpEo8QSUo8WKWQAQEREBOvXr8fDhw+Rl5cHCwsLHDhwAJWVlS21C0LIJ9zc3DBv3jxMmzYN3t7ebMchpM3cvHkTc+bMwdKlS7Fjxw624xDSbNHR0Rg9ejRcXFwwceJEvHz5Ei4uLmzHIqRda7FCptqAAQMQEhKClStXYv369ejbty98fHxaejeEEAAcDgfu7u6YP38+nJyccOLECbYjEdLqrl27hkmTJsHFxQUHDhxgOw4hzZKUlITFixfDzMwMeXl5CAwMxPHjx6kVhpAGaPFCBgDExMSwY8cOxMXFwcrKCpMnT8bgwYPx6NGj1tgdIV0ah8PBkSNH8OOPP2Lx4sX44YcfaBRB0mn9/vvvcHR0xOzZs+Hu7k4XPpMOKzs7Gxs2bIChoSFu3LiBI0eO4NmzZxg8eDDb0QjpMFqlkKmmra2NM2fOICgoCBwOB8OGDYOLiwsSExNbc7eEdDkcDgebN2/G6dOn8fPPP2PChAnIzc1lOxYhLWrPnj1YsGAB1q5dixMnTkBIqFX/hRHSKlJSUvD9999DT08PZ86cwf79+xEXF4dFixbRRK6ENBKHaaMhxhiGgaenJzZs2ICkpCTMmjULGzZsgLGxcVvsnpAuIzg4GE5OTuByufjrr78wZMgQtiMR0ixlZWVYsmQJ/v77b0FXSkI6mujoaOzbtw8eHh5QUlLC6tWrsWzZMkhISLAdjZCOKqDNTmdxOBw4OTkhLi4OHh4eePbsGfr06QMHBwc8efKkrWIQ0ukNHDgQISEh6NWrF0aMGIHvv/8e79+/ZzsWIU0SHx+PIUOGwNfXF76+vlTEkA6FYRj4+/tj4sSJ6NOnDx49eoSjR48iISEB69atoyKGkGZq83Z5ISEhODk5ITIyEpcuXUJaWhoGDhwIe3t73L17l+agIaQFKCsr49q1azh37hxOnjwJCwsLPH36lO1YhDSKr68vrKyswOFw8OzZM5pHg3QYeXl5+PXXX2FsbAwbGxvk5ubCy8sLL168wPz58yEmJsZ2REI6BdY6GAsJCWHKlCl49uwZbty4gffv32P06NEwMTHBoUOHUFBQwFY0QjoNJycnPH/+HJqamhgyZAh+/PFHlJWVsR2LkM8qKyvDihUr4OjoCCcnJwQFBaFHjx5sxyLki4KDg/HNN99AU1MTmzZtgrW1NZ4/f47AwEA4OjrSdV2EtLA2u0amIWJiYuDu7o5Tp06Bx+PByckJq1evhrm5OdvRCOnQGIaBu7s71q9fDwUFBezZswfTp0+nEZ9IuxMcHIyvv/4amZmZOHz4MJydndmORMhnpaSkwNPTE6dPn0Z4eDh69eqFefPmYdGiRZCXl2c7HiGdWUC7KmSqFRQU4OzZszh27Biio6MxaNAgLFq0CFOnToWMjAzb8QjpsLKzs7F9+3YcOXIEFhYW+OWXXzB06FC2YxGCiooKbNu2DXv27IGNjQ1OnjwJLS0ttmMRUqfCwkL8888/OHPmDO7fvw8FBQXMmjULc+bMgZWVFdvxCOkq2mch87F79+7B3d0dPj4+4HA4cHBwwJw5c2Bvbw8RERG24xHSIYWFhWHNmjUICAiAs7Mzdu7cCR0dHbZjkS7q/v37WLZsGRISErB582Z899131AWHtDv5+fm4ffs2fH19cfnyZfB4PNja2sLFxQWOjo4QFRVlOyIhXU37L2SqFRQUwMfHB5cuXcL169chIyODCRMmwMXFBTY2NtRFhpAm8PX1xerVq5GUlIQZM2bgp59+Qs+ePdmORbqI3NxcuLq64sSJExg/fjwOHToEXV1dtmMRIpCTkwMfHx94enrizp07AABbW1tMnToVkyZNoq5jhLCr4xQyH3v37h08PDzw119/ITo6Gj169MCUKVMwadIkDB48mM7kEdIIlZWVOHfuHHbs2IHExETMnDkTGzduhJGREdvRSCdVWVmJ48ePY9OmTZCWlsbBgwfh6OjIdixCAAAJCQnw9fXFlStXEBAQAGFhYYwZMwZOTk5wdHSErKws2xEJIR90zELmY2FhYTh//jy8vLwQFxcHNTU1ODo6YvLkybC2tqamXkIaiMfjwcPDAzt37sTr168Fk9aamJiwHa1Dq6ioQFZWFrKzs1FcXCwYNS4/Px8Mw4DL5UJaWhoAICUlBSkpKSgpKUFJSQlcLpfN6K3i33//xfr16/HmzRusWrUKP/30EyQlJdmORbqwsrIyBAQE4MqVK7h69SoSExOhoqKCcePGYfz48bC3txe8Rwkh7UrHL2Q+FhUVBW9vb3h5eSE0NBRycnIYP348HB0dMWbMGGoCJqQB+Hw+rl69is2bNyMsLAxDhw7FypUrMWXKFAgLC7Mdr11KSEhAZGQkXr9+jfj4eLx+/RqJiYlIT09HYWFhk7erqKgIFRUV9OjRAwYGBoKbmZkZ1NXVW/AZNE1RUVGDP+CFhoZi3bp1uHfvHpycnLB7927o6em1ckJC6vZxq0tgYCDev3+P3r17w8HBAWPGjMGoUaM65YkEQjqZzlXIfCwxMVFQ1Dx8+BAAYGVlBXt7e9jZ2aF///70oYyQz2AYBnfu3MFvv/2Gy5cvo3v37li0aBEWLlwIBQUFtuOxpqCgAAEBAQgKCkJISAhCQkKQl5cHANDU1IS+vj709fWhp6cHNTU1qKioCFpYZGRk0K1bNwCArKwshISEwOPxUFRUBAAoLS1FYWEhsrKykJWVhYyMDGRkZNQokLKzswEA6urqsLS0hKWlJYYPH44hQ4ZAXFy8zX4Pt2/fxtKlSxEdHf3Zlu+YmBjs2rULf//9NywsLLB//34MHz68zXISAgCvXr1CQEAA/Pz84O/vj+zsbKipqcHGxgZjxoyBra0tNDU12Y5JCGmczlvIfCwvLw9+fn64efMmbt68ieTkZCgqKsLW1hZ2dnYYO3YsNDQ02I5JSLsVGxuLw4cP488//wTDMHBxccGCBQu6xBxPfD4fQUFBuHLlCvz9/REaGgqGYWBiYiIoJCwtLWFqatomXaTy8/MRFhYmKKKePXuG+Ph4iImJYfDgwbCxsYGjoyP69u3bahk8PDwwb948QXfEuuZ6iYuLw7Zt2/D333+jd+/e2Lp1KyZPnkwDs5BWxzAMoqOjERAQgPv37+P+/ftIS0uDpKQkRowYgTFjxmDMmDHo27cvHY+EdGxdo5D5VEJCAvz8/ODn54fr16+juLgYPXr0wNChQzFs2DDY2dmhe/fubMckpN0pLCzE6dOncezYMcTGxqJfv36YN28eZs+eDSUlJbbjtRiGYRAYGIhLly7hn3/+QWpqKgwNDTFmzBiMHj0ao0aNgqKiItsxBZKTk+Hv7487d+7Az88PqampMDIywrRp0zBjxowWLWoOHDiA1atXAwA4HA4sLS3x5MkTwf0vX77E7t274eHhAQMDA2zevBnTp0+nQVhIqykuLkZISAiCg4Px+PFjBAYGIisrCzIyMhg2bBhGjBiBESNGoH///jRtAyGdS9csZD5WWlqKBw8eICAgAAEBAXj69CkqKythaGiIkSNHYuTIkRg1ahQ1ORPyiZCQEPz22284d+4cysvLMXbsWLi4uGDSpEkd9sNCfn4+Ll68iEOHDiEqKkrQZ37ChAkYNmwY2/Ea7MWLF7h06RI8PDwQHx8PS0tLLFq0CLNnz25yqxHDMNiyZQu2bdtW675nz56Bw+Fg586d8PLygqGhIVxdXTF79mzqwktaVFVVFV68eIHg4GAEBwfjyZMniI6ORlVVFTQ0NGBlZYXhw4djxIgRMDc3p+OPkM6NCplPlZSU4OHDh4LC5smTJ6isrIS+vj4GDx6MQYMGYfDgwTA1NaULAQnBh/eMp6cnTp8+jfv370NVVVXQEjB06NAO0XXjzZs32LVrF86ePQtRUVHMmTMHixYtgpmZGdvRmoVhGAQEBOD48ePw8vKCuLg4li1bhtWrVzeqBY3H42HhwoU4c+YM+Hx+jftERESgpaWFN2/ewMLCAq6urpgyZQq1wJAWkZqaKuhGGRISgsDAQOTn50NSUhL9+vWr0b2TRlgkpMuhQuZLSktLERQUhMDAQDx+/BiPHz9GQUEBJCQk0L9/f0FxM2jQIKipqbEdlxBWvX79Gh4eHrhw4QKio6Ohra2N6dOnY8aMGRgwYECztp2amtri17K9efMGO3bswNmzZ6GtrY3169c3q9WiPcvKysKJEyfwyy+/4P3791i2bBm+//77L3aRKykpwZQpU3Dnzh1UVVXVuQ6Xy8Xff/8NJyen1ohOuojExEREREQgIiICz549w5MnT5CWlgZhYWGYmJhg4MCBGDhwIKysrNC7d29qbSGEUCHTWHw+HzExMQgODkZQUBAeP36MmJgY8Pl8dO/eHf3794eFhQUsLS1hYWEBZWVltiMTworIyEhcuHABFy5cQHx8PPT19eHk5IRJkyZhwIABjTpjn5KSAiMjI+zZswfLli1rditPaWkpdu/eDTc3N2hra2Pjxo2YPXt2l2hlLSkpwbFjx7Bv3z5UVlZix44dWLRoUZ0fCnNycjBu3DiEhYWBx+PVu00ul4s9e/ZgzZo1rRmddBJFRUWIiooSFC0RERGIjIxEQUEBOBwO9PT0YGFhIShaLC0tO+XJBUJIs1Eh0xIKCgoEfXVDQ0MREhKCd+/eAQB0dHRqFDaWlpZQVVVlOTEhbSskJATnz5/H5cuXkZCQAHV1dTg4OMDR0RGjR48WDElcn7///htz5swBAAwaNAhnzpyBgYFBk7Jcv34dS5cuRV5eHjZv3oxvv/22w17T0xxFRUXYvn07fv31V/Tp0we///57jVHo3rx5AxsbGyQnJ6OysvKL29PR0cGbN2+oSxmpobprWHR0NF68eIGQkBC8fPkSfD4f0tLSMDQ0RO/evQVdw8zNzdvVQBqEkHaNCpnWUlBQgMjIyBp9e2NiYsAwDOTl5Wv84e7duzfMzc3pjBPpEiIjI+Hj4wMfHx+EhIRAUlIS9vb2cHBwgL29PVRUVGo9ZtGiRfjjjz9QWVkJERERcDgcbNu2DevWrWtw95Ly8nJs2LABBw4cwMyZM/Hzzz9Td1B8GGVs8eLFCA4Oxq5du7Bq1SqEhYXBzs4OBQUFDSpiql27dg3jxo1rxbSkPSovL0dcXBxiY2Px6tUrxMbGIiYmBi9evEBJSQmEhYWhr68PMzMzmJqaom/fvjA1NaUJUQkhzUWFTFvKzc1FaGiooBk9MjIS0dHRKCsrg7CwMAwMDAR/4KsLHH19/S55tph0DcnJyfj333/h4+ODe/fuobKyEhYWFrC3t4e9vT0GDRoELpcLXV1dvH37tsZjhYSEYG5ujjNnzqB3795f3M/EiRPx+vVrHDlyRNC6Qz6oqqrC7t27sWXLFvTv3x8REREoLS2tsQ6HwwGXyxW0uPD5fPB4PHz8L2T8+PG4cuVKm2YnbScpKQmvXr0SFCvVhUtiYiL4fD6EhITQvXt3GBoawtjYGCYmJjAzM4OJiQkkJCTYjk8I6XyokGkP6mp6j42NRVVVFbhcLnR0dNCjRw/07t1bUOCYmppCRkaG7eiEtJji4mL4+/vjxo0buHnzJhISEiAnJ4dhw4bV++FYREQEfD4f69atw7Zt2+qcYT4qKgpfffUVZGRk4OPjA319/dZ+Kh3W9u3bsXnzZnA4HCgoKEBOTg5ycnJQUFCAoqIiZGVlISsrC3l5ecH3srKykJOTE3yloeo7toyMDLx58wYJCQmCgqW6eCkuLgYAyMvLw9DQEEZGRoKboaEhDA0NISYmxvIzIIR0IVTItFdlZWWIjY3Fy5cvERMTg5cvX+Lly5eIjY1FeXk5AEBTUxNGRkbo2bMnDAwMaty+dM0BIe3dq1evcOPGDfz++++IjIzE5/5UCQsLw9DQEGfPnoWlpaVg+dOnTzF27FiYmZnB29sbcnJybRG9w8rPz0d2djbGjx8PHo+HgIAAaGlpsR2LtKCioiK8efOmxi0hIUHwfXVLnIiICPT09NCrVy9BoVJdtNTV/ZMQQlhAhUxHU1VVhcTERMTExCAmJgavXr1CfHw84uPjkZycDOBDFxAtLa1axY2enh50dXUhLy/P8rMgpOE+vj7mc7hcLvh8Pr777jts3boVycnJGDJkCCwtLeHl5UVnihshOzsb1tbWAIAHD/qaKb8AACAASURBVB5QAdiBlJSU4N27d0hKSqpVsLx58wbZ2dmCddXV1aGnp1fr1qNHD2hpadHwxoSQ9o4Kmc6koqICycnJSEhIQEJCAl68eIHo6GgkJCTg7du3gjkgunXrBg0NDfTo0aPWzcDAALKysiw/E0L+R09PD4mJiQ1eX1hYGD169MD79++hqqqKu3fvQkpKqvUCdlLJyckYPHgwDAwM4OfnRx9q24GysjKkpaUhNTUVaWlpSEhIqPP7ap/7W29kZETvC0JIR0eFTFfxaZHz8T+/hIQEwcWaQM1/furq6tDQ0BB8rV6mrq7eIWZsJx1bampqg6654HK5EBYWBp/Pr9Fys3z5cri5uTW7q6W7uzuWLl362XXs7e1x/fr1Bm/z+fPnOHr0KAICApCSkgIA0NDQgK6uLkaPHg07Ozv069ev3vdZXFwcDA0NMXDgQDx+/LjhT6YRIiIiMHDgQGzatAmurq6tsg/yYdSvzMxMpKamIiMjA+np6UhNTUVSUhJSUlKQlJSEpKQkFBUVCR4jKSkJHR0daGpqQlNTE927d6/1PbW+E0I6uYDOP/sbAQCIiooKzsTVpbS0VND1IDk5GampqXj37p1gIILk5GQUFhYK1hcXF4eWlhY0NDSgra0NDQ0NaGpqCr7X0tKCmpoancUlzXLv3j0AH7pLfnzOhcvlQkFBoVahraKigqSkJLi5ucHd3R3Ozs5tdr3YkCFDGrQen8+Hq6sr9u/fj6VLl+LChQswMjISDGHr7u4OV1dXuLq64unTp+jfv3+d2zl9+jQAIDg4GNHR0V8cua0pTE1NsX37dvz4449wcHBAnz59GvX4qKgonDx5EjweD4cPH27xfO1dVlYWMjMzkZaWhrS0NGRmZiIlJQVZWVlITU1Feno6MjIykJOTU+NxsrKygqJES0sLAwcOhJaWFjQ1NaGjowMtLS3q7kcIIQCoRYY0WHFxseAMYfVZwtTUVCQnJyMlJUXwj7masLAw1NTUoK2tDVVVVaioqEBVVRVKSkpQVlaGmpoalJWVBTcqesinbt68iZiYGKioqAgKFVVV1XonzKuqqoKxsbFg0syW4u7ujhs3bsDb27vWfXFxcejbty8SExMbNC/Nxo0bsXPnThw7dgxLliypc50NGzZgz5499RYyfD4fOjo6UFFRQVhYGNatWwc3N7fGP7EGqKqqwuDBg6GmpoZ///33i+uXlJTgwoULcHd3x9OnTwEADg4ODXpse8bj8ZCdnY3s7Gzk5OQIipTqn7Ozs2ssy8rKQkVFheDxoqKiUFFRgaamJlRUVKCurg41NTWoqqoKjm0NDQ2oqqpCXFycxWdKCCEdBnUtIy2roqJCUNwkJycjLS0N7969Q2ZmJjIyMpCZmYmsrCxkZ2eDx+MJHsfhcAQFjrKycq2iR11dXfC9iooKFBQUWHyWpDUVFxfjv//9L2bOnAkzM7NGPfb8+fOYM2cOYmJi0LNnzxbL5Ofnh/DwcKxdu7bWfStWrEBmZibOnz//xe3ExMSgT58+MDc3x7Nnz+pdLz8/H+rq6njw4EGdhcyNGzewZMkSeHp6YsCAAVBVVUVycjK43NZpZPf19YWjoyNCQkJgbm5e5zohISH4888/8ccff6CkpAQcDkdwXd7o0aNx586dVsnWFLm5ucjLyxN8rev7T4uW3NzcWttRUlIS3BQVFaGkpFTj71Z1YaKmpkaz1RNCSMujQoawp7qgqT6LmZGRgaysLGRlZQmKnuzsbGRmZtbqeiEiIgJlZWXIyclBXl6+UTc629m+5efnC/r2GxgYwMXFBTNnzmxQYTJixAioqanh4sWLrR0TwIehbLW0tODr64sRI0Z8cf3Vq1fj119/xe7du7F+/fom73f69Ono3bs3tmzZAjMzM0RERMDHxwcTJ05s8jY/h2EYmJqaYsiQITh+/LhgeWFhIc6fP4/Dhw8jMjISIiIidY4uZ2VlheDg4BbJkZ+fj8LCQhQXF6O4uBhFRUXIy8ur8XNhYSEKCgrqLVY+JSQkBHl5eSgoKAj+TnxcpCgpKUFFRaVW4UKtyIQQwioqZEjHwOPx6ixyqj+gfHrLz89HXl4eysrKam2rW7dudRY41UWRjIwMpKWlISsrCykpKUhKSkJaWhpycnKCn2m0n9ZTUFBQo/8/l8sFj8eDmZkZXFxcMH369DrnNsnLy4OKigrOnTuHadOmtUnWgwcP4sSJE4iMjGzQ+paWlggNDcX169dhb2/fpH3m5uZCU1MT0dHR0NPTw6+//orVq1fD0dGxzq5vLWXr1q04efIkkpKSEBISguPHj+Ps2bOorKwEn8//7Dw/xsbGuHr1qqDQKC4uRkFBAQoLCwU/FxcXIy8vr8bP1QVJ9bKSkpJ69yElJQUpKSlIS0tDRkZGMHHnx8VJ9fefLqORGgkhpEOiQoZ0bu/fv6+32Kmv+Pn4jG71SG51kZeXr1HYVBc61TdZWVlIS0tDSkoK4uLikJWVhbCwMOTk5MDlciEtLQ0xMTFISEhAQkICYmJikJaWbrXuQR3Fp4VMNQ6HIyhqBgwYAGdnZ8ycOROqqqoAAE9PT8yaNQtZWVltciE0wzDo1asXVq1a9cURzappaGggLS0NwcHBsLKyatJ+Dx8+jMuXL8Pf3x/AhzlfNDQ0wDAMUlJSWm2ywlu3bsHOzg7du3fH27dvISoqWuMakM8RExMTTOT7sW7dukFcXLzWyYVPl31pHSUlJYiKirb0UyaEENK+0ahlpHPr1q2bYLjopigtLRWcHc7Pzxd8X9fP1WeYCwoKkJKSIvi+uLgY5eXldXZpqY+cnByEhYUhKysLUVFRSEpKCj64SUlJQURERPAVgKArVvW6ABp8P4fDqfXBX0ZGpka3merCqy3UN9wwwzCCbktPnz5FSEgI1qxZAysrK/zf//0fUlJSoKur22ajOV2/fh1paWmYO3dugx/zpfNG/fr1Q3h4uODnulpZTp8+jVWrVgl+VlJSwoQJE+Dl5YWzZ8/WeR1Pc/H5fMFcPtUT7zbmHJiUlBTu378vKOyrWzgJIYSQ5qAWGULaUFVVFQoLC1FRUYGSkhKUlZXh/fv3KC4uRmVlJQoKClBVVYX8/HzweDwUFRWhvLwcpaWlKC0tRXl5OQoLCwXbqaqqEnwPfGiBqu5OV9f9Lam6FeljcnJyDZpf6HOFUVVVFZ4/f97gHEJCQuDz+YLWrrNnz2LcuHENfnxT2dvbw8DAoFHDCvfv3x8hISG4evUqvvrqq3rXe/bsGQYMGFCrkImIiMCwYcOQnp4OCQkJwXJfX19MnDgRJiYmiIqKatoTagBFRUVs3rwZenp68PX1xT///IPc3Nx6r42pJicn16hCnhBCCGkAapEhpC0JCwuzOkldQwqd6guqP1ZdTH2sqKioxshzjSmYPn3sxyoqKhpcyHA4HHA4HMFQ34qKig266L65Xr16hVu3buHnn39u1ONGjBiBkJAQhIaGfraQqc+pU6dQVFQkaFX71IsXL/DkyZMmd1v7EmlpaZSXl8PBwQEODg44duwYHj16hCtXrsDT0xOvX7+GiIgIeDxejRab9+/ft0oeQgghXRu1yBBC2pWSkpIvDqZQfa2MqakpvvnmG8ycORO//PIL/Pz8PjuscUtZvnw5oqKiBBN2NlT18MvGxsaIjIyst/WqrhaZyspKaGhowMfHp87JN6tHRFuyZAmOHTvW6OfUEJKSkjh69Ci+/vrrOu+PjIzEv//+i3/++QfPnz+HsLCwYCCAqqqqBrXWEUIIIQ0UIMR2AkII+Vh9H3arL+Y2MDDAxo0bERcXh/DwcKxcuRKqqqpQVVVFampqq+crLCzEmTNn8J///KfRjzU2NsaGDRvw4sUL7N27t971qudf+Zivry+UlJTqLGIAYP78+QCAc+fO1TlaX3Pl5eWhtLT0s5N+9u3bFxs3bkRoaCiSk5Nx+PBhjBkzBqKiotQqQwghpMVR1zJCSLtVfe2Fqqoq5s2bh9mzZ6Nv3751rtu/f3+kpaUhPj4eBgYGrZbp1KlTkJKSwuTJk5v0+O3bt6OyshKurq549eoVli1bBmNjY3A4HMTHx8PX1xcHDx6EsLBwjckwT58+jW+++abe7fbp0wdWVlZ48uQJLl++jNmzZzcpX33u378PISGheifE/JSGhgYWL16MxYsXo7i4mEYVI4QQ0uKoaxkhpF0pKyuDhIQEZGVl4ezsjFmzZmHYsGFf7JbE4/GgrKyMrVu3YsWKFa2SjWEYGBoawtnZGVu3bm3WtkJDQ3HkyBEEBAQgNTUVVVVVUFBQQO/evTF69Gg4OztDT08PycnJ0NbWFjxu4MCBePz4cY1tJSYmQk9Pr8YyVVVVpKenNyvjx5YsWYLQ0FA8efKkxbZJCCGENAPNI0MIaV94PB5u3LgBOzs7wfDRDeXi4oLw8HA8f/6crsdoQYWFhdDV1cV3330HV1dXtuMQQgghAF0jQwhpb7hcLiZMmNDoIgYA1q1bh8jISFy9erUVknVdR48eRVVVFZYsWcJ2FEIIIUSAWmQIIZ2Ko6Mj3rx5g6dPn9aa54Y0XkZGBkxMTLBo0SLs3LmT7TiEEEJINWqRIYR0LgcOHEBiYmKzr2EhHyxbtgwyMjLUpYwQQki7Q4UMIaRT0dXVhZubG9zc3HDr1i2243Rohw8fhre3N06fPg1paWm24xBCCCE1UCFDCOl0Fi1aBGdnZ0ydOhWhoaFsx+mQvLy8sGrVKmzfvh0jR45kOw4hhBBSCxUyhJBOpaCgACdOnEBUVBS4XC6++uorhIeHsx2rQ7l27Rpmz56NJUuW4IcffmA7DiGEEFInKmQIIR0en8+Hn58fnJ2doaamhjVr1sDU1BQXL15Enz59MGLECNy5c4ftmB3C77//DkdHRzg7O+PAgQNsxyGEEELqRYUMIaTDSktLw9atW6Gvrw9bW1skJibi0KFDSEtLw+nTp2Fra4tr165hwoQJ+Oqrr3Do0CHQQI11q6iowNq1a7Fw4UL88MMPOHHiBISFhdmORQghhNSLy3YAQghprPDwcPzyyy84d+4cZGVl4eLigm+++Qa9e/euta6oqCj++usv9OrVC2vWrMGtW7dw6tQpKCsrs5C8fXr16hVmzZqFV69e4c8//8TcuXPZjkQIIYR8EbXIEEI6BIZh4OfnBwcHB5ibm+PRo0fYu3cv3r59i3379tVZxFTjcDj46aefcO/ePURGRsLExASnTp0Cn89vw2fQ/pSXl+O///0vzM3NISQkhNDQUCpiCCGEdBhUyBBC2rWysjK4u7vD2NgYY8eOBY/Hw40bN/Dy5UusXLkS4uLiDd7W0KFDER4ejpkzZ2Lx4sUYMmQIgoODWzF9++Xr64s+ffpg165d+PHHH/Hw4UP07NmT7ViEEEJIg1EhQwhplyoqKvDbb7/BwMAAq1atgpWVFSIiInD9+nWMHTsWHA6nSduVlZXFwYMHERISAjExMQwaNAjjxo1DUFBQCz+D9odhGPj4+KB///5wdHSEubk5YmJi4OrqClFRUbbjEUIIIY1ChQwhpF2prKysUcDMnDkT7969w5kzZ9CnT58W24+pqSkCAgJw8+ZNFBUVYejQobC2tsbFixdRUVHRYvtpD4qKinD8+HH069cPkydPRnR0NA4cOICLFy9CW1ub7XiEEEJIk1AhQwhpF/h8Pi5dugQTExMsX74c48aNQ1xcHPbv3w8VFZVW2+/YsWMRGBiIO3fuQFpaGs7OztDR0cEPP/yAqKioVttva+Pz+QgKCsLSpUuhqamJ1atXw9zcHA8fPsTcuXOxYsUKuLi4oLi4mO2ohBBCSJNwGBqLlBDCIoZh4OnpiU2bNiE+Ph4uLi7YtGkTunfvzkqepKQknDx5EqdOnUJycjKMjIwwbdo0TJ48WXBRfHtVUVGBhw8f4vLly/Dy8kJKSgpMTEywcOFCuLi4QF5eXrCup6cnlixZAllZWZw5cwZDhw5lMTkhhBDSaAFUyBBCWBMWFoaVK1fi4cOHmDVrFjZv3txuLjjn8/l4/PgxPD098c8//+Ddu3dQVFSEtbU1Ro8ejZEjR8LIyIjVuVYqKysRHh6OgIAA3LlzB/fv30dJSQn69OmDqVOnwsnJCSYmJvU+PiMjA/Pnz8fNmzexdu1abN++HSIiIm34DAghhJAmo0KGENL2srOzsXHjRpw8eRIDBw7EwYMH0b9/f7Zj1YthGERERMDf319QMBQVFUFKSgr9+vWDhYUFTE1N0bNnT+jr60NTU7NF919VVYV3797h9evXiIuLQ1hYGEJDQxEZGYmKigooKyvD2toaNjY2sLGxgb6+fqOe24kTJ7BmzRqYmJjg7NmzMDQ0bNH8hBBCSCugQoYQ0nYYhsGZM2ewbt06iImJYc+ePXB2dm7yCGRs4fF4iIiIQGhoKEJDQxESEoKoqCiUlpYCAMTFxdGjRw+oqKhATU0NSkpKUFJSgoSEBGRkZAAAUlJSEBERQXl5ueBx+fn5KCkpQXZ2NjIyMpCVlYX09HQkJiYKBiCQk5ODmZkZLCwsYGlpCQsLC/Tq1avZv8OYmBjMnTsX0dHR2LVrF1asWNHhXhdCCCFdChUyhJC28fr1ayxduhR37tzBggUL4ObmJvhQ31mkpqYiPj4e8fHxSExMRGZmJjIzM5GdnY3s7GyUlZWhoKAAwIeRxHg8HsTExCAhIQEOhwM5OTmIi4tDSUkJKioqUFFRgbKyMgwMDGBgYAB9fX0oKSm1Wv6Kigps2bIFe/fuhYODA06fPg05OblW2x8hhBDSDFTIEEJa18ddl/T19XH8+HEMGjSI7VjkMx48eIBZs2ZBTEwMFy9ehKWlJduRCCGEkE8FtN/hdwghHV5iYiJsbGywfPlyrF+/HiEhIVTEdADDhw/H8+fP0bNnTwwdOhQHDhxgOxIhhBBSCxUyhJBW8ddff8HMzAxZWVl4/PgxfvrpJ3C5XLZjkQZSUlLC1atXsX79eqxZswazZ89GSUkJ27EIIYQQASpkCCEtqqysDAsWLICLiwvmz5+PZ8+ewcLCgu1YpAmEhYWxdetWXLt2Dbdu3cKwYcOQmJjIdixCCCEEABUyhJAWFBsbi4EDB8LLywve3t74+eefISYmxnYs0kx2dnYIDQ2FsLAw+vfvD39/f7YjEUIIIVTIEEJaxrVr1zBw4EBISEggNDQUEydOZDsSaUHa2toICAiAtbU17OzssGfPHrYjEUII6eKokCGENNuBAwcwceJEjB8/Hnfv3kX37t3ZjkRagaSkJC5evIhNmzbB1dUVS5YsAY/HYzsWIYSQLoqGXyaENFllZSUWLFgADw8PuLm5YfXq1WxHIm3Ey8sLc+bMwahRo3DhwgVISUmxHYkQQkjXQvPIEEKapqSkBE5OTnjw4AEuXboEe3t7tiORNvb06VM4ODhAVVUVV69ehZaWFtuRCCGEdB00jwwhpPHy8vJgZ2eH4OBg3Lp1i4qYLmrAgAF49OgRKioqMGjQIISHh7MdiRBCSBdChQwhpFEyMzMxfPhwJCcnIygoCIMHD2Y7EmGRnp4eAgMDoaurC2trazx+/JjtSIQQQroIKmQIIQ2WlZUFGxsblJeXIzAwEEZGRmxHIu2AoqIi/Pz8MGzYMNja2uLu3btsRyKEENIFUCFDCGmQvLw82Nvbo6ioCLdv36brIUgN3bp1g5eXF6ZMmYJx48bB29ub7UiEEEI6OSpkCCFfVFRUBFtbW+Tl5eH+/fvQ1dVlOxJph4SFhXHq1CnMmTMH06dPx6VLl9iORAghpBPjsh2AENK+8Xg8TJ8+XXBNjI6ODtuRSDsmLCyMEydOQEJCArNmzUJlZSWcnZ3ZjkUIIaQTokKGEPJZK1asQEBAAPz9/dGjRw+245AOgMPh4ODBgxAREcHXX38NCQkJTJo0ie1YhBBCOhkqZAgh9dqzZw9+++03eHt7Y9CgQWzHIR3Mvn37UFZWhhkzZsDLywtfffUV25EIIYR0IlTIEELq5O/vj40bN8LNzQ0TJkxgOw7pgDgcDo4cOYKKigpMmzYN165dw6hRo9iORQghpJPgMAzDsB2CENK+pKWlwdLSEgMHDsTly5fB4XDYjkQ6sKqqKsycORO3bt2Cv78/LC0t2Y5ECCGk4wugQoYQUgOPx8PIkSORl5eH4OBgSEtLsx2JdAIVFRWYOHEiIiIi8PjxYxo0ghBCSHMF0PDLhJAa3NzcEBoaikuXLlERQ1qMqKgoLl26BGVlZYwbNw4FBQVsRyKEENLBUSFDCBGIjo7Gtm3bsH37dpiYmLAdh3Qy0tLSuHbtGgoKCjBjxgzweDy2IxFCCOnAqGsZIQTAhy5lgwcPhoiICB48eABhYWG2I5FO6unTpxg1ahS+/vprHD16lO04hBBCOqYAGrWMEAIAOHz4MKKiovD8+XMqYkirGjBgAM6ePYtp06bB0tIS8+fPZzsSIYSQDoi6lhFCkJeXhx07dmDIkCHo1asXOBwOtLS02I7Vbp0/fx4cDgccDgfdunVrs21euHAB/fr1g7i4uGDdqKioFtl/czQl+9SpUzF//nwsX74coaGhX9zHvn37BPugY7N+rXFsdqQs7f046cjvc0LaIypkCCHYunUrhIWFcfnyZTAMAzMzM7YjNVtxcTF69uzZKnPgzJw5EwzDwMbGps22+fDhQ8yaNQtjx45FVlYW4uPj280HtaZm//bbbzF8+HDMmDHjixf/r1u3jo7NBvjca9ES+23MNlrjffIl7f046cjvc0LaIypkCOni3rx5g2PHjmHr1q2QlZVlO46AlJQUhg0b1uTHMwwDPp8PPp/fgqnYc+nSJTAMg5UrV0JKSgr6+vpISkpCnz592I72RfVlNzU1xZkzZ1BcXIyFCxeyHbPBOuqx2RL77Wzvq/amI7/PCWEDXSNDSBd38OBBqKmpYcGCBWxHaVHS0tJ4/fo12zFaTFJSEgBAUVGxwY8xNzfHzJkzMWvWLFbnbflcdjU1NXh4eMDW1hZnzpyBi4tLW8drc2wdmy2x3872vqrWEd4rhJDaqEWGkC6sqKgIp0+fxooVK8Dl0nmN9qyqqqrRj1FXV8fGjRuhq6uLUaNG4cSJE8jPz2+FdJ/3peyjR4/GihUrsGLFCrx9+7aNUhHyPx3lvUIIqYkKGUK6sBMnToDP59c7atTLly8xfvx4yMrKQkJCAtbW1nj48GGt9bKysrBixQro6upCVFQUysrKmDJlCp4/f15jvfLycmzatAm9evWChIQEFBQU4ODggH///VfwD7z6Yt2SkhI8fPhQcLFrYwotb29vweM4HA7ev39f5/LExETMmDEDcnJyUFRUxIQJE+o82/zy5UtMmjQJsrKykJSUxPDhwxEYGNjgPHVp6DarM/v4+ACA4ALgQYMGfXEf165dQ3JyMvbt24fCwkIsWrQIampqmDJlCi5fvozy8vJ2k3337t3Q1tbG3LlzG9RtiY7N//0eGvNafLzf/Pz8Gss4HA527NgB4MNw7B8vnzZtWr3ZG5tlx44dgm183EXvxo0bguVKSko1HsPj8XDhwgXY2tpCTU0N4uLi6Nu3Lw4cONAi3dw60nuFEPIRhhDSZRkZGTHLly+vtdzMzIyRlZVlrK2tmcDAQKaoqIh5+vQpY2pqyoiKijL37t0TrJuamsp0796dUVVVZa5evcoUFRUxUVFRzMiRI5lu3boxQUFBgnUXLFjAyMrKMrdu3WJKS0uZ9PR0Zt26dQwA5u7duzUySEpKMkOHDm3W83N0dGQAMGVlZXUud3R0ZIKCgpji4mLm9u3bjLi4ODNgwIAa68bFxTFycnKMpqYmc+vWLaaoqIiJiIhgxo4dy+jq6jJiYmKNztWUbdb3XBojOjqacXV1ZXR0dBgAjJycHLNgwQLm7t27DJ/PZz37s2fPGBEREebQoUP1rkPH5v+01Gthb2/PCAkJMfHx8bXWHzx4MPP3339/cRtNyVLf79HS0pJRVFSssczX15cBwOzcuZPJzc1lsrKymIMHDzJCQkLMunXram3DzMyM0dTUrLW8odr7e4UQwjAMw9yjQoaQLiokJIQBwAQHB9e6z8zMjAHAPHr0qMbyiIgIBgBjZmYmWPb1118zABgPD48a66alpTFiYmKMpaWlYJmenh4zZMiQWvszNDRk5cOir69vjeXTpk1jADBZWVmCZU5OTgwAxtPTs8a6KSkpjJiYWJMKmaZssyU/4PD5fObevXvMggULGDk5OQYAY2pq2i6yu7q6MtLS0kxSUlKd99Ox2bxjs679+vn5MQCYZcuW1Vg3MDCQ0dHRYSorK7+4jaZkaWwhM2rUqFrrzpkzhxEREWEKCgpqLG9uIVOtPb9XCCHMPepaRkgXdeHCBejp6WHAgAF13t+tWzcMHDiwxrK+fftCQ0MD4eHhSEtLA/ChS4SQkFCt4VjV1NRgYmKCkJAQJCcnAwDs7e0RFBSERYsW4fHjx4IuO7GxsRg1alQLP8Mv+/S5a2trAwBSU1MFy27cuAEAsLOzq7GuhoYGDA0Nm7Tf1thmY1TPYdGtWzeIiIg06rGtnX3z5s3Q0NDAihUr6l2Hjs0PWuq1sLGxgbm5Of744w/k5OQIlru5uWHVqlUN6jrX2sfFhAkTcPfu3VrLzczMUFlZiRcvXjR7H3Vpz+8VQghdI0NIl8QwDC5evIgZM2aAw+HUuY6iomKd96moqAAAMjMzUV5ejoKCAvD5fMjKytbqb1890WFcXBwA4MiRIzhz5gwSEhJgY2MDGRkZ2Nvbw8vLq5We6ed9Oty0qKgoAAj63JeXl6OoqAjdunWDlJRUrcdX/y4aozW22VCvXr3C5s2bYWBggEGDBuGvv/7CxIkT4e/vX+uakbq0RXYxMTG4u7vD29sb3t7eda5Dx2bLvxZr165FaWkpjh49CuDDsXL//v0GjWbYFsdFQUEBNm3ahL59+0JeXl7wOn733XcAgNLS0mbvK1Ag1QAAIABJREFU42Md4b1CCKFChpAuKSoqComJiZgyZUq969Q3QWFmZiaAD/+IxcTEICcnBy6Xi8rKSjAMU+fN2toawIezm3PnzoWfnx/y8/Ph7e0NhmEwZcoU/PzzzzX2U1+B1ZbExMQgLS2N9+/fo7i4uNb9ubm57WKbn5ORkYGDBw/CysoKRkZG2L17N/r27QtPT0+kp6fj5MmTsLa2btDvu62yjxo1CnPnzsWKFSvq/IBKx2bLvxYzZsyAtrY2Dh8+jPLycuzfvx8LFy6EtLR0q2UREhJCRUVFreV1jRbm4OCA7du3Y+HChXj16hX4fD4YhsEvv/wC4MPJmebqiO8VQro6KmQI6YL8/f0hJycHCwuLetcpLi5GeHh4jWWRkZFITU2FmZkZ1NXVAQBTpkwBj8erc8SoPXv2QEdHBzweDwAgJyeHly9fAgBERERga2srGK3n6tWrNR4rISFR40OOkZERfvvtt6Y94WYYN24cgP91E6mWnZ2N2NjYdrPNuowfPx6amppYtWoVxMXFcfz4caSnp8PLywtTp06FmJhYo7fZVtnd3NxQWFiIvXv31rqPjs0PWvK14HK5WLlyJTIzM7F//36cP3/+s937WiKLuro6UlJSaixLT0/Hu3fvaiyrqqrCw4cPoaamhhUrVkBZWVlQTJSVlTU44+d05PcKIV1am1+WQwhhnaOjI+Po6Fjv/WZmZoykpCQzbNgw5vHjx0xxcXG9I0NlZGQw+vr6TI8ePZhr164x+fn5TE5ODuPu7s5ISEgwFy5cEKwrKyvLjBw5kgkPD2fev3/PZGRkMFu2bGEAMDt27KiRwd7enpGVlWXevXvHBAUFMVwul4mOjm7088RnLqj+dPn69esZAExYWJhgWXx8PKOgoFBj5KEXL14wdnZ2jIqKSpMu9m/KNptyEXC/fv2YXbt2MW/fvm10RrazMwzD7N27lxEXF2fevHkjWEbHZvOOzc+9FoWFhYysrCzD4XAYFxeXRmVvSpbly5czAJhDhw4xRUVFTHx8PDN9+nRGU1Oz1sX+o0ePZgAwe/fuZbKyspjS0lLG399fMKrY7du3a6zf2Iv9O/p7hZAuikYtI6SrqaqqYuTl5Zlff/211n1ubm4MAAYAo6mpyTx58oSxtrZmpKSkGHFxcWbkyJFMYGBgrcfl5OQwa9asYXr06MGIiIgwysrKzNixY2t9uHj+/DmzePFixtjYmJGQkGAUFBSYQYMGMSdOnKg1pOnLly+Z4cOHM5KSkoy2tjZz5MiRBj9HLy8vwfOovs2ePZt59OhRreUbN25kGIaptXz8+PGC7cXGxjKTJk1iZGRkBMPgXrlyhbGxsRGsP3/+/Abna8w263ouqGPUrrbUVtnLy8sZQ0NDZsaMGXRsNvPYrG+/n/ruu+8YAEx4eHiDszc2S7X8/HxmwYIFjLq6OiMuLs4MGzaMefr0KWNpaSlYf/369QzDMExWVhazePFiRltbmxEREWFUVVWZefPmMRs2bBCsa2lpWeM4+fT3yIaO/D4npAO4x2GYFuhYSgjpMF6+fAljY2M8e/YMlpaWbMch5LN8fX3h6OiIoKAgmhyQEELIxwLoGhlCupiwsDBwuVyYmJiwHYWQL3JwcMCIESOwadMmtqMQQghpZ6iQIaSLCQ8Ph7GxMbp168Z2FEIaZMeOHbh9+zbu3bvHdhRCCCHtCBUyhHQxERERMDU1ZTtGk306H0hdty1btlC2TmTYsGEYM2YMfvzxR7ajfBa9/oQQ0ra+PF0vIaRTefXqFf7v//6P7RhN1p4v62vP2Tq6nTt3YuDAgbhx4wbs7e3ZjlMnev0JIaRtUYsMIV0In89HcnIyunfvznYUQhplwIABGD9+PH788UcqGAghhACgQoaQLiU9PR3l5eVUyJAOaefOnQgLC4OPjw/bUQghhLQDVMgQ0oW8ffsWAKCjo8NyEkIar2/fvpg8eTJ++ukn8Pl8tuMQQghhGRUyhHQhGRkZAAB1dXWWkxDSNNu2bUN0dDQuX77MdhRCCCEso0KGkC4kNzcXUlJSEBUVZTsKIU3Su3dvODo6Yu/evWxHIYQQwjIqZAjpQvLy8iAnJ8d2DEKaZe3atXj69CmCgoLYjkIIIYRFVMgQ0oXk5eVBXl6e7RiENMvQoUMxaNAg7N+/n+0ohBBCWESFDCFdSGlpKSQlJdmOQUizrVmzBt7e3oiPj2c7CiGEEJZQIUNIF8Lj8SAiIsJ2DEKabcqUKdDT08OBAwfYjkIIIYQlVMgQ0oXweDxwuVy2YxDSbMLCwvj2229x6tQp5OTkCJaXlZXhr7/+woMHD1hMRwghpC1wGJoimZBOKT4+HtHR0cjNzRXcvLy8kJubi169eiErKwu5ubkoLCzEpk2b8P3337MdmZBGKSkpgY6ODr777jtYW1vj1KlT8PDwQElJCdzd3bF48WK2IxJCCGk9AXRqlpBOKjExEY6OjhAWFoawsDA4HA74fD54PB7S09NrrGtra8tSSkKarqSkBObm5tizZw9cXV0hKiqKiooKiIiI4P3792zHI4QQ0sqoaxkhndSYMWNgbGwMhmFQUVGB8vJyVFZW4tNGWDk5OZiZmbGUkpDG4fP58PPzw7Rp06CpqYmAgAAUFBQAACoqKgAAQkJCKC8vZzMmIYSQNkCFDCGd2MqVKz97P5fLxVdffQUhIfpTQNo/Pp+PkSNHwtbWFj4+PuDxeODxeLWKcwDUIkMIIV0AfXohpBObO3cupKSk6r2fz+dj7NixbZiIkKYTEhLCiRMnICUlBT6f/9l1qUWGEEI6PypkCOnEJCQksGTJknqHXObz+bCxsWnjVIQ0Xa9evXDlypXPtiIyDEOFDCGEdAFUyBDSyf3nP/9BVVVVnfcZGhpCS0urjRMR0jwjR47E8ePH672fChlCCOkaqJAhpJPT0dGBo6NjrVYZUVFRTJgwgaVUhDTPN998g9WrV0NYWLjWfQzD0DUyhBDSBVAhQ0gXsGrVKlRWVtZYVlFRQcMukw5t3759GDduXK1JXvl8PrXIEEJIF0CFDCFdwIgRI2BmZlbjugIul4vhw4ezmIqQ5hESEsK5c+dgaGhYo8WRz+dTiwwhhHQBVMgQ0kV8PBQzh8PB0KFDISkpyWIiQppPSkoKt2/fhoKCQo1uZmVlZSymIoQQ0haokCGki3B2doacnByAD60x48aNYzkRIS1DQ0MD165dg4iICDgcDgAqZAghpCugQoaQLkJMTAzLli2DkJAQKisr6foY0qlYWFjg7Nmzgp+pkCGEkM6P++VVCCEdDcMwyM/PR2lpKUpLS1FYWAiGYdCnTx9wOBxISkoiJycHV69eRWlpaY3HiomJQUJCosYyaWlpcLlcSEpKQkJCAtLS0oJlhLCpvLwcpaWlyM/Ph7GxMZYuXYqjR48iMzMTfn5+gvWKiorA4/FqPPbTY1hISAiysrIAAHl5eYiLi0NCQkKwjBBCSPvCYRiGYTsEIaR+xcXFePfuHVJTU5GVlYXs7Gzk5OQgJycH2dnZyM7ORlZWFoqKipCfn4+ysrI2OxstKioKSUlJyMrKQlpaGoqKilBSUoKysrLge0VFRSgqKkJLSwuamppQUFBok2yk40lPT0dKSgrS0tJqHOOfHvdFRUUoLi5GYWFhvXMktbSPi3gFBQXBsf3xMa6srAwVFRVoaGhAW1sb3bp1a5NshBDSRQVQIUMIy3JychAfH4+4uDgkJCQgOTkZycnJePfuHZKTk1FQUCBYV1hYuNYHqOrCQUZGBnJychAXF4e4uDjk5eUhISEBcXFxwRllGRkZhIWFITY2FrNnzwaXy4W0tHSNPKWlpbWGrs3LywPwoagqLS0VfIgsKytDSUkJCgoKUFRUhJycHGRlZSErK6vGB9GKigrBtiQkJKCjoyMobLp37w4dHR307NkTPXv2hLq6emv9qgmL+Hw+kpKSEBcXh7i4OMHxXf01JSWlxnEnLi5e4/hWUlISHPPS0tKQkpKCjIwMxMXFBcV0dQsKwzDYtWsX3Nzcamzv08IiPz8fH/8LrKioQElJSY0WzbKyMhQUFKC4uBhlZWWC4/zTEwrVXz+mrKwMTU1NaGtrQ1tbG1paWtDX10fPnj1hYGBQ671HCCGkUaiQIaQt8Pl8JCQk4Pnz54iOjsarV68ExUtubi6AD126dHV1oaWlBS0tLejo6EBTU1Pwvbq6OpSUlFokT0lJSZuOWFZYWCgo0FJSUmp8eH337h0SExNRUlIC4MMoVNVFjYGBAXr37g0zMzP06tWLurJ1AO/fv8f/a+/Og6K6sj+Af1la0i0tICLIqiJKkEVEwQYBRSEuJAQixsRsWqBTUXRm1MEsWkmcpIIkGiJOSUliMinigqMxIS6AIspiRBBQTGRV9qURbIRuoOn7+2N+9NiCAkb6sZxPVZfwlntPw1Hvef3ufTdv3kRubi4KCwuVhUtxcbGyUDEwMICVlZVygP/wYN/MzAxmZmY9bm8cqPb2dujo6DyLt9RvCoUC9fX1qKqqUuZ2VVWVSsFWXl6uvMXNxMQE06dPV+a6g4MDHB0dYWFhoda4CSFkmKJChpBnraOjA9evX0dubi5yc3ORl5eHGzdu4MGDB9DS0lJekZ0+fTqmTZumHLRbWlqqPOdltKmurlYOersHvkVFRbh9+zY6Ojqgo6ODmTNnwsnJSfmaM2cOdHV1uQ591GpsbERWVhby8vKQm5uL/Px8FBYWQi6XQ1dXFzNmzFAO0h/O+WdVkA9HnZ2dKCsrQ1FRkcoFjaKiIty5cwcAMH78eMyaNQtOTk5wdHTE7NmzMXPmTJXlpQkhhFAhQ8ifVltbi6ysLGRnZyM9PR3p6emQSqUQCoWYPn067Ozs4OLiAhcXFzg7O9OzWwZILpfj9u3buHXrFgoKCpCdnY1r166htrYWWlpamDFjBlxcXDB//nx4eHjAzs5OuQQvebZKS0uRlpaG9PR0pKWl4Y8//oBCocCkSZMwc+ZMlVy3tbWlgfcASSQS5Ofnq+T69evX0dbWBl1dXTg5OSnz3N3dHYaGhlyHTAghXKJChpCBam5uxvnz55GYmIjk5GSUlpZCU1MTM2fOhLu7O9zd3SESiTBt2jQaUA+iyspKZGZmIiMjA5mZmcjJyUFnZydMTEzg4+ODF154AX5+fjAxMeE61GGruLgYiYmJSExMxMWLF3H//n2MHTsWc+fOhYeHB0QiEebNm0cD6kHU1dWFgoICZGRkKHO9uLgYmpqasLOzg5+fH/z8/ODl5QU+n891uIQQok5UyBDSH9euXcPp06dx7tw5XL16FYwxzJkzB35+fpg/fz7mzZuHcePGcR3mqCaVSpGdnY20tDQkJycjLS0NHR0dcHBwwAsvvIAlS5bA29ubPiV4AqlUiqSkJJw9exaJiYkoKSmBUCiEj48PFi1aBA8PDzg6OtJcJY7V19cjMzMTqampSExMREFBAfh8Pjw9PeHn5wd/f3/MmDGD6zAJIWSwUSFDyOMUFBQgPj4eP/74I4qKimBsbAwvLy/4+/vD39+flhEe4qRSKdLT05GcnIzk5GTk5OTAwMAAy5cvR3BwMJYsWQIej8d1mJyTyWRISkpCfHw8Tp06hQcPHsDZ2RmLFy/G4sWL4eXlhTFjxnAdJnmCuro6XLp0CcnJyUhISEB1dTXs7OwQHByMlStXws7OjusQCSFkMFAhQ8jDCgsLERsbi2PHjuHu3buwsbHBihUrEBwcDGdnZ67DI39CSUkJjh8/jvj4eGRnZ8PQ0BBBQUEICQmBq6sr1+GplUKhQFJSEr799lv8+uuvkMlk8PLyQnBwMAIDA+l2vGFMoVDg8uXLiI+Px4kTJ1BTUwN7e3u89tprWLt2Lf1uCSEjCRUyhHR0dOCnn35CTEwMUlJSYGVlhdWrVyM4OBhOTk5ch0cGQWlpKY4fP464uDjk5+fD2dkZ69evx+uvvz6in+1RV1eHQ4cO4eDBgygrK4OXlxdee+01BAYGYuLEiVyHR54xhUKBtLQ0xMfH4/Dhw5BIJAgICMD69euxaNEimsNHCBnuqJAho5dEIkFUVBT2798PsViMZcuW4S9/+QuWLFkyqpdBHm0yMzNx4MABHDt2DDweD2vWrEF4eDhMTU25Du2ZuXXrFj799FMcP34cY8eOxdtvv43169fD1taW69CImshkMhw/fhwHDhxAeno6pk2bhm3btuGdd96hWwcJIcMVFTJk9Ll//z6ioqLw1VdfQaFQYOPGjVi/fj09hG6Uu3fvHr777jvs2bMHjY2NCA0NRXh4OMzMzLgO7akVFBRg165diI+Ph52dHbZu3YqVK1fS6laj3M2bNxEdHY1Dhw7BxMQE7733HtauXUsFDSFkuKFChowecrkcX3/9NXbt2gUNDQ1s3rwZmzdvhr6+PtehkSGkvb0dsbGxiIiIQH19PcLCwvDRRx8Nq+f/1NbWYsuWLThy5AhmzpyJnTt3IigoiD5pJCoqKirw+eef45tvvoGxsTEiIyOxcuVKrsMihJD+okKGjA7Z2dlYt24dCgoK8I9//ANbtmyBnp4e12GRIay7oNmxYwfGjRuH/fv3Y/ny5VyH9USMMRw8eBDh4eEwMDBAZGQkgoKCaC4EeaLKykp89NFH+Pbbb7F06VLs378fkydP5josQgjpSypdniMjmlwuR3h4ONzc3CAUCpGXl4dPPvmEihjSJx0dHWzYsAG///47PDw84O/vj1WrVqGlpYXr0HpVWVkJLy8vbNiwASEhIbh58yZeeeUVKmJIn8zNzREbG4vU1FTcuXMH9vb2+Ne//sV1WIQQ0icqZMiI1dTUhOXLlyM6Olq5ItlgPSROLpfjm2++gaurKwwNDWFgYAAXFxdER0ejo6Oj3+10dXXhwIEDcHd3h56eHng8HkxNTbFs2TJER0fjzp07gxL/n6GrqwsNDY1+vWJjYx/bzhdffKE8ztzcXI3v4MmMjY0RFxeHs2fPIjU1Fe7u7igtLeU6LBWZmZmYO3cumpqakJWVhcjISAgEgkHpq6urC1999RVmzZoFgUAAPT09+Pj4IDk5ecDtUK4PrVz39PTE9evXsWXLFoSFhWHdunUD+veLEELUjhEyAhUWFjIbGxtmbm7Orl27Nuj9vfHGGwwAe++991hdXR0Ti8UsIiKCAWD+/v79bue1115jmpqaLCIiglVUVDCpVMqKi4vZ+++/zzQ0NJihoeEgvound/36dQaABQQEPPYYb29vdvDgQcYYYy0tLWzatGls+fLlPY5zcnJiZmZmgxbrn1FeXs5mz57NDA0NWWpqKtfhMMYY++GHH5iOjg578cUXmUQiGdS+5HI58/f3Zzwej+3bt4+JxWJWWlrK1qxZwzQ0NNjhw4f73Rbl+tDO9VOnTjGhUMg8PT1ZY2Mj1+EQQkhvLlIhQ0acO3fuMAsLCzZ37lxWU1M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+       "layout": "IPY_MODEL_a61fa785bcd94d969bfcf1db8519b18f",
+       "style": "IPY_MODEL_8c28db83fce34637bdd7748ba767049f"
+      }
+     },
+     "a65f8bdb171f4f2f9151474ce3917d34": {
+      "buffers": [
+       {
+        "data": 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+      "state": {
+       "description_width": ""
+      }
+     },
+     "ffc48e4cb69f4dc29a84bb8fcd549a2e": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "width": "40px"
+      }
+     },
+     "ffc7339827924081b31eaf3d7d1ddc75": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "ffd8dd5266ae4c2595ddff62670e5adb": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "layout": "IPY_MODEL_253d5d8b6a8f432b900acd98d1e66fe7",
+       "style": "IPY_MODEL_8055f2c695fe4cd39fd66bb5a36029fb",
+       "value": "<b>Wrapper parameters:</b>"
+      }
+     },
+     "ffe1032c2ca8476ab605ef413dcdd345": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "ffe191a1044d42148ffc646000cb7b2d": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ButtonModel",
+      "state": {
+       "description": "Select",
+       "layout": "IPY_MODEL_e1945f34280c4a4c9a99736aa3dc28b5",
+       "style": "IPY_MODEL_211bd7186505458b9bd1ffaf651ba954"
+      }
+     },
+     "ffe2268d680c44308bfa765fe4c2eced": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "margin": "10px 0px 0px 10px",
+       "width": "50%"
+      }
+     },
+     "fff5104cdc7543a38b5c0f423afb44a2": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "fffb5bacbe1f4bed893a808a03331002": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ButtonModel",
+      "state": {
+       "description": "Add",
+       "layout": "IPY_MODEL_5dac7c00ef7c41ffbb6bf7dc45f4c12b",
+       "style": "IPY_MODEL_6eaf719f8b014901b0a1c567011c6173"
+      }
+     },
+     "fffc9a243a104194a730e0d10de1e275": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     }
+    },
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/setup.py b/setup.py
index 8539edc8..d6456997 100644
--- a/setup.py
+++ b/setup.py
@@ -40,7 +40,7 @@
             'matplotlib>=1.5.1',
             'lxml', 'wget',
             'seaborn',
-            'graphviz', 'openpyxl', 'ipywidgets'
+            'openpyxl', 'ipywidgets'
             ],
         extras_require={
             'docs': [

From f28a38aa7381033d6df985777ab6bfd71ca0dcaa Mon Sep 17 00:00:00 2001
From: aatishpr <aatishpr@buffalo.edu>
Date: Tue, 9 Nov 2021 14:49:56 -0500
Subject: [PATCH 3/4] documentation updated

---
 docs/chemml.optimization.rst            | 20 --------
 docs/include_CMLWR5Search.rst           |  1 -
 docs/include_CMLWR7Visualize.rst        |  8 ++--
 docs/include_CMLWR8Store.rst            |  4 +-
 docs/index.rst                          |  2 +-
 docs/ipython_notebooks/Convo_nets.ipynb |  4 +-
 docs/wrapper_docs/cheml.GA_DEAP.rst     | 62 -------------------------
 7 files changed, 9 insertions(+), 92 deletions(-)
 delete mode 100644 docs/wrapper_docs/cheml.GA_DEAP.rst

diff --git a/docs/chemml.optimization.rst b/docs/chemml.optimization.rst
index 953acac2..a5ba6fff 100644
--- a/docs/chemml.optimization.rst
+++ b/docs/chemml.optimization.rst
@@ -1,26 +1,6 @@
 Optimization module
 ===================
 
-.. Submodules
-.. ----------
-
-.. chemml.search.ga_binary module
-.. -----------------------------
-
-.. .. automodule:: chemml.search.ga_binary
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-.. chemml.search.ga_deap module
-.. ---------------------------
-
-.. .. automodule:: chemml.search.ga_deap
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-
 .. Module contents
 .. ---------------
 
diff --git a/docs/include_CMLWR5Search.rst b/docs/include_CMLWR5Search.rst
index 08238e6c..78e09466 100644
--- a/docs/include_CMLWR5Search.rst
+++ b/docs/include_CMLWR5Search.rst
@@ -1,4 +1,3 @@
-.. include:: wrapper_docs/chemml.GA_DEAP.rst
 .. include:: wrapper_docs/sklearn.GridSearchCV.rst
 .. include:: wrapper_docs/sklearn.cross_val_predict.rst
 .. include:: wrapper_docs/sklearn.cross_val_score.rst
diff --git a/docs/include_CMLWR7Visualize.rst b/docs/include_CMLWR7Visualize.rst
index 95529d8f..0f80c908 100644
--- a/docs/include_CMLWR7Visualize.rst
+++ b/docs/include_CMLWR7Visualize.rst
@@ -1,4 +1,4 @@
-.. include:: wrapper_docs/chemml.decorator.rst
-.. include:: wrapper_docs/chemml.hist.rst
-.. include:: wrapper_docs/chemml.scatter2D.rst
-.. include:: wrapper_docs/pandas.plot.rst
+.. include:: ./wrapper_docs/chemml.decorator.rst
+.. include:: ./wrapper_docs/chemml.hist.rst
+.. include:: ./wrapper_docs/chemml.scatter2D.rst
+.. include:: ./wrapper_docs/pandas.plot.rst
diff --git a/docs/include_CMLWR8Store.rst b/docs/include_CMLWR8Store.rst
index 26b1f52d..90f15f3a 100644
--- a/docs/include_CMLWR8Store.rst
+++ b/docs/include_CMLWR8Store.rst
@@ -1,2 +1,2 @@
-.. include:: wrapper_docs/chemml.SaveFile.rst
-.. include:: wrapper_docs/chemml.SavePlot.rst
+.. include:: ./wrapper_docs/chemml.SaveFile.rst
+.. include:: ./wrapper_docs/chemml.SavePlot.rst
diff --git a/docs/index.rst b/docs/index.rst
index 3943cb1f..8f217299 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -118,7 +118,7 @@ Please cite the use of ChemML as:
    CMLWInputFile
    CMLWInputFileGenerator
    ./ipython_notebooks/read_excel.ipynb 
-   _./ipython_notebooks/pyscript_usage.ipynb
+   ./ipython_notebooks/pyscript_usage.ipynb
    ./ipython_notebooks/simple_ml_model.ipynb
    ./ipython_notebooks/GA_tutorial.ipynb
 
diff --git a/docs/ipython_notebooks/Convo_nets.ipynb b/docs/ipython_notebooks/Convo_nets.ipynb
index 504266a1..2473b9c6 100644
--- a/docs/ipython_notebooks/Convo_nets.ipynb
+++ b/docs/ipython_notebooks/Convo_nets.ipynb
@@ -333,7 +333,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -347,7 +347,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.8.12"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/docs/wrapper_docs/cheml.GA_DEAP.rst b/docs/wrapper_docs/cheml.GA_DEAP.rst
deleted file mode 100644
index f936b0ea..00000000
--- a/docs/wrapper_docs/cheml.GA_DEAP.rst
+++ /dev/null
@@ -1,62 +0,0 @@
-.. _GA_DEAP:
-
-GA_DEAP
-========
-
-:task:
-    | Search
-
-:subtask:
-    | genetic algorithm
-
-:host:
-    | chemml
-
-:function:
-    | GA_DEAP
-
-:input tokens (receivers):
-    | ``evaluate`` : a function that receives a list of individuals and returns the score
-    |   types: ("<type 'function'>",)
-
-:output tokens (senders):
-    | ``best_individual`` : pandas dataframe of the best individual
-    |   types: ("<class 'pandas.core.frame.DataFrame'>",)
-    | ``best_ind_df`` : pandas dataframe of best individuals after each iteration
-    |   types: ("<class 'pandas.core.frame.DataFrame'>",)
-
-:wrapper parameters:
-    | ``func_method`` : string, (default:algorithm_1)
-    |   a method of the GA_DEAP class that should be applied
-    |   choose one of: ('algorithm_1', 'algorithm_2', 'algorithm_3', 'algorithm_4')
-
-:required packages:
-    | ChemML, 0.4.1
-    | pandas, 0.20.3
-    | deap, 1.2.2
-
-:config file view:
-    | ``##``
-    |   ``<< host = chemml    << function = GA_DEAP``
-    |   ``<< func_method = algorithm_1``
-    |   ``<< mut_float_dev = 1``
-    |   ``<< init_pop_frac = 0.35``
-    |   ``<< crossover_type = Blend``
-    |   ``<< chromosome_type = (1,)``
-    |   ``<< n_generations = 20``
-    |   ``<< Evaluate = @evaluate``
-    |   ``<< chromosome_length = 1``
-    |   ``<< crossover_pop_frac = 0.35``
-    |   ``<< crossover_prob = 0.4``
-    |   ``<< Weights = (-1.0,)``
-    |   ``<< mut_float_mean = 0``
-    |   ``<< bit_limits = ((0, 10),)``
-    |   ``<< mut_int_lower = (1,)``
-    |   ``<< mut_int_upper = (10,)``
-    |   ``<< pop_size = 50``
-    |   ``<< mutation_prob = 0.4``
-    |   ``>> id evaluate``
-    |   ``>> id best_individual``
-    |   ``>> id best_ind_df``
-    |
-    .. note:: The documentation page for function parameters: 

From a33f050383e2217d82e98ff34cfc86f37e185ebb Mon Sep 17 00:00:00 2001
From: aatishpr <aatishpr@buffalo.edu>
Date: Tue, 9 Nov 2021 18:34:33 -0500
Subject: [PATCH 4/4] Updated tutorials and regression metrics

---
 chemml/__init__.py                        |   4 +-
 chemml/models/keras/mlp.py                |   1 +
 chemml/utils/utilities.py                 |   2 +-
 docs/ipython_notebooks/Convo_nets.ipynb   |  49 ++++-
 docs/ipython_notebooks/ga_hyper_opt.ipynb | 109 ++++-----
 docs/ipython_notebooks/images/Parity.png  | Bin 125428 -> 125149 bytes
 docs/ipython_notebooks/ml_model.ipynb     | 255 ++++++++++++++++------
 7 files changed, 293 insertions(+), 127 deletions(-)

diff --git a/chemml/__init__.py b/chemml/__init__.py
index 7823137a..ee11d8d2 100644
--- a/chemml/__init__.py
+++ b/chemml/__init__.py
@@ -1,6 +1,6 @@
 # __name__ = "chemml"
-__version__ = "0.8"
-__author__ = ["Mojtaba Haghighatlari (mojtabah@buffalo.edu)", "Johannes Hachmann (hachmann@buffalo.edu)"]
+__version__ = "1.0"
+__author__ = ["Aditya Sonpal (adityaso@buffalo.edu)", "Garuav Vishwakarma (gvishwak@buffalo.edu) ", "Aatish Pradhan (aatishpr@buffalo.edu)","Mojtaba Haghighatlari (mojtabah@buffalo.edu)", "Johannes Hachmann (hachmann@buffalo.edu)"]
 
 
 # import sys
diff --git a/chemml/models/keras/mlp.py b/chemml/models/keras/mlp.py
index 77843d55..9fbf00b3 100644
--- a/chemml/models/keras/mlp.py
+++ b/chemml/models/keras/mlp.py
@@ -130,6 +130,7 @@ def save(self, path, filename):
         obj_dict['path_to_file'] = path +'/'+ filename+'.h5'
         obj_df = pd.DataFrame.from_dict(obj_dict,orient='index')
         obj_df.to_csv(path+'/'+filename+'_chemml_model.csv')
+        print("File saved as "+path+"/"+filename+"_chemml_model.csv")
         
     def load(self, path_to_model):
         """
diff --git a/chemml/utils/utilities.py b/chemml/utils/utilities.py
index a1c690cd..328920ab 100644
--- a/chemml/utils/utilities.py
+++ b/chemml/utils/utilities.py
@@ -384,7 +384,7 @@ def regression_metrics(y_true, y_predicted, nfeatures = None):
     metrics_dict['AE'] = [list(ae)]
     metrics_dict['SE'] = [list(se)]
 
-    var = np.mean(np.square(y_predicted - y_mean))
+    var = np.mean(np.square(y_true - y_mean))
     
     metrics_dict['ME'] = np.mean(e)
     # mean absolute error
diff --git a/docs/ipython_notebooks/Convo_nets.ipynb b/docs/ipython_notebooks/Convo_nets.ipynb
index 2473b9c6..7be21af6 100644
--- a/docs/ipython_notebooks/Convo_nets.ipynb
+++ b/docs/ipython_notebooks/Convo_nets.ipynb
@@ -15,7 +15,18 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-11-09 18:22:23.552207: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2021-11-09 18:22:23.552291: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
+      "/mnt/c/Aatish/UB/Mr. Hachmann/master_chemml_wrapper_v2/chemml/chemml/datasets/base.py:87: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
+      "  features = df.drop(['smiles', 'density_Kg/m3'],1)\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from chemml.datasets import load_organic_density\n",
@@ -62,7 +73,7 @@
      "output_type": "stream",
      "text": [
       "Tensorising molecules in batches of 100 ...\n",
-      "500/500 [==================================================] - 0s 601us/step\n",
+      "500/500 [==================================================] - 1s 1ms/step\n",
       "Merging batch tensors ...    [DONE]\n"
      ]
     }
@@ -156,6 +167,17 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-11-09 18:23:03.337511: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
+      "2021-11-09 18:23:03.337613: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
+      "2021-11-09 18:23:03.337645: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (Aatish-HP): /proc/driver/nvidia/version does not exist\n",
+      "2021-11-09 18:23:03.337984: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -262,10 +284,17 @@
       "__________________________________________________________________________________________________\n"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-11-09 18:23:04.812435: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras.callbacks.History at 0x7fac474e07b8>"
+       "<keras.callbacks.History at 0x7feda4198be0>"
       ]
      },
      "execution_count": 7,
@@ -312,8 +341,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean Absolute Error = 18.407 kg/m^3\n",
-      "R squared = 0.903\n"
+      "Mean Absolute Error = 16.518 kg/m^3\n",
+      "R squared = 0.935\n"
      ]
     }
    ],
@@ -322,9 +351,9 @@
     "\n",
     "y_pred = model.predict([xatoms_test,xbonds_test,xedges_test])\n",
     "y_pred = y_scale.inverse_transform(y_pred)\n",
-    "metrics_dict = regression_metrics(target_test, list(y_pred.reshape(-1,)))\n",
-    "mae = metrics_dict['MAE']\n",
-    "r_2 = metrics_dict['r_squared']\n",
+    "metrics_df = regression_metrics(target_test, list(y_pred.reshape(-1,)))\n",
+    "mae = metrics_df['MAE'].values[0]\n",
+    "r_2 = metrics_df['r_squared'].values[0]\n",
     "\n",
     "print(\"Mean Absolute Error = {} kg/m^3\".format(mae.round(3)))\n",
     "print(\"R squared = {}\".format(r_2.round(3)))"
@@ -333,9 +362,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:chemml_env] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-chemml_env-py"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/docs/ipython_notebooks/ga_hyper_opt.ipynb b/docs/ipython_notebooks/ga_hyper_opt.ipynb
index ea34de6d..9223c540 100644
--- a/docs/ipython_notebooks/ga_hyper_opt.ipynb
+++ b/docs/ipython_notebooks/ga_hyper_opt.ipynb
@@ -16,6 +16,16 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-11-09 18:23:58.580965: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2021-11-09 18:23:58.581035: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
+      "/mnt/c/Aatish/UB/Mr. Hachmann/master_chemml_wrapper_v2/chemml/chemml/datasets/base.py:87: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
+      "  features = df.drop(['smiles', 'density_Kg/m3'],1)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -91,7 +101,7 @@
     "    krr = KernelRidge(alpha=individual[0], kernel=individual[1], degree=individual[2])\n",
     "    krr.fit(features[:400], density[:400])\n",
     "    pred = krr.predict(features[400:])\n",
-    "    mae = regression_metrics(density[400:],pred)['MAE']\n",
+    "    mae = regression_metrics(density[400:],pred)['MAE'].values[0]\n",
     "    return mae"
    ]
   },
@@ -162,33 +172,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>(4.3428571428571425, linear, 1)</td>\n",
-       "      <td>0.105177</td>\n",
-       "      <td>0.001049</td>\n",
+       "      <td>(2.928571428571429, linear, 1)</td>\n",
+       "      <td>0.102514</td>\n",
+       "      <td>0.000390</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>(4.3428571428571425, linear, 1)</td>\n",
-       "      <td>0.105177</td>\n",
-       "      <td>0.001099</td>\n",
+       "      <td>(1.1057547599530448, linear, 1)</td>\n",
+       "      <td>0.095582</td>\n",
+       "      <td>0.000230</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>(1.3309342128829402, linear, 1)</td>\n",
-       "      <td>0.096677</td>\n",
-       "      <td>0.000536</td>\n",
+       "      <td>(1.1057547599530448, linear, 1)</td>\n",
+       "      <td>0.095582</td>\n",
+       "      <td>0.000312</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>(1.1297049580471703, linear, 5)</td>\n",
-       "      <td>0.095685</td>\n",
-       "      <td>0.000572</td>\n",
+       "      <td>(1.1057547599530448, linear, 1)</td>\n",
+       "      <td>0.095582</td>\n",
+       "      <td>0.000143</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>(1.1297049580471703, linear, 5)</td>\n",
-       "      <td>0.095685</td>\n",
-       "      <td>0.001062</td>\n",
+       "      <td>(1.1057547599530448, linear, 1)</td>\n",
+       "      <td>0.095582</td>\n",
+       "      <td>0.000309</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -196,11 +206,11 @@
       ],
       "text/plain": [
        "                   Best_individual  Fitness_values  Time (hours)\n",
-       "0  (4.3428571428571425, linear, 1)        0.105177      0.001049\n",
-       "1  (4.3428571428571425, linear, 1)        0.105177      0.001099\n",
-       "2  (1.3309342128829402, linear, 1)        0.096677      0.000536\n",
-       "3  (1.1297049580471703, linear, 5)        0.095685      0.000572\n",
-       "4  (1.1297049580471703, linear, 5)        0.095685      0.001062"
+       "0   (2.928571428571429, linear, 1)        0.102514      0.000390\n",
+       "1  (1.1057547599530448, linear, 1)        0.095582      0.000230\n",
+       "2  (1.1057547599530448, linear, 1)        0.095582      0.000312\n",
+       "3  (1.1057547599530448, linear, 1)        0.095582      0.000143\n",
+       "4  (1.1057547599530448, linear, 1)        0.095582      0.000309"
       ]
      },
      "execution_count": 5,
@@ -221,7 +231,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'alpha': 1.1297049580471703, 'kernels': 'linear', 'degree': 5}\n"
+      "{'alpha': 1.1057547599530448, 'kernels': 'linear', 'degree': 1}\n"
      ]
     }
    ],
@@ -281,45 +291,45 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>(1.1297049580471703, linear, 5)</td>\n",
-       "      <td>0.095685</td>\n",
-       "      <td>0.001405</td>\n",
+       "      <td>(1.1057547599530448, linear, 1)</td>\n",
+       "      <td>0.095582</td>\n",
+       "      <td>0.000271</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>(1.1297049580471703, linear, 5)</td>\n",
-       "      <td>0.095685</td>\n",
-       "      <td>0.000581</td>\n",
+       "      <td>(1.1057547599530448, linear, 1)</td>\n",
+       "      <td>0.095582</td>\n",
+       "      <td>0.000260</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>(0.37125764950137985, linear, 2)</td>\n",
-       "      <td>0.091373</td>\n",
-       "      <td>0.000673</td>\n",
+       "      <td>(1.1057547599530448, linear, 1)</td>\n",
+       "      <td>0.095582</td>\n",
+       "      <td>0.000471</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>(0.37125764950137985, linear, 2)</td>\n",
-       "      <td>0.091373</td>\n",
-       "      <td>0.000532</td>\n",
+       "      <td>(1.1057547599530448, linear, 1)</td>\n",
+       "      <td>0.095582</td>\n",
+       "      <td>0.000116</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>(0.37125764950137985, linear, 2)</td>\n",
-       "      <td>0.091373</td>\n",
-       "      <td>0.000353</td>\n",
+       "      <td>(1.0210089362161252, linear, 4)</td>\n",
+       "      <td>0.095203</td>\n",
+       "      <td>0.000316</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                    Best_individual  Fitness_values  Time (hours)\n",
-       "0   (1.1297049580471703, linear, 5)        0.095685      0.001405\n",
-       "1   (1.1297049580471703, linear, 5)        0.095685      0.000581\n",
-       "2  (0.37125764950137985, linear, 2)        0.091373      0.000673\n",
-       "3  (0.37125764950137985, linear, 2)        0.091373      0.000532\n",
-       "4  (0.37125764950137985, linear, 2)        0.091373      0.000353"
+       "                   Best_individual  Fitness_values  Time (hours)\n",
+       "0  (1.1057547599530448, linear, 1)        0.095582      0.000271\n",
+       "1  (1.1057547599530448, linear, 1)        0.095582      0.000260\n",
+       "2  (1.1057547599530448, linear, 1)        0.095582      0.000471\n",
+       "3  (1.1057547599530448, linear, 1)        0.095582      0.000116\n",
+       "4  (1.0210089362161252, linear, 4)        0.095203      0.000316"
       ]
      },
      "execution_count": 8,
@@ -330,20 +340,13 @@
    "source": [
     "fitness_df_resume"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:v2_0.7]",
+   "display_name": "Python [conda env:chemml_env] *",
    "language": "python",
-   "name": "conda-env-v2_0.7-py"
+   "name": "conda-env-chemml_env-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -355,7 +358,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.8.12"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/docs/ipython_notebooks/images/Parity.png b/docs/ipython_notebooks/images/Parity.png
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diff --git a/docs/ipython_notebooks/ml_model.ipynb b/docs/ipython_notebooks/ml_model.ipynb
index 851a8451..5068f6d9 100644
--- a/docs/ipython_notebooks/ml_model.ipynb
+++ b/docs/ipython_notebooks/ml_model.ipynb
@@ -16,10 +16,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
     "from chemml.datasets import load_organic_density\n",
     "molecules, target, dragon_subset = load_organic_density()"
    ]
@@ -33,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,9 +47,10 @@
     "xscale = StandardScaler()\n",
     "yscale = StandardScaler()\n",
     "\n",
-    "dragon_subset = xscale.fit_transform(dragon_subset)\n",
-    "X_train, X_test, y_train, y_test = train_test_split(dragon_subset, target, test_size=0.25, random_state=42)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(dragon_subset.values, target.values, test_size=0.25, random_state=42)\n",
     "\n",
+    "X_train = xscale.fit_transform(X_train)\n",
+    "X_test = xscale.transform(X_test)\n",
     "y_train = yscale.fit_transform(y_train)"
    ]
   },
@@ -59,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -67,45 +71,45 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
-      "8/8 [==============================] - 0s 2ms/step - loss: 0.8398\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.7672\n",
       "Epoch 2/20\n",
-      "8/8 [==============================] - 0s 2ms/step - loss: 0.4404\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.2712\n",
       "Epoch 3/20\n",
-      "8/8 [==============================] - 0s 2ms/step - loss: 0.2558\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.1306\n",
       "Epoch 4/20\n",
-      "8/8 [==============================] - 0s 2ms/step - loss: 0.1091\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.1710\n",
       "Epoch 5/20\n",
-      "8/8 [==============================] - 0s 3ms/step - loss: 0.0748\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.1697\n",
       "Epoch 6/20\n",
-      "8/8 [==============================] - 0s 3ms/step - loss: 0.0516\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.0862\n",
       "Epoch 7/20\n",
-      "8/8 [==============================] - 0s 3ms/step - loss: 0.0477\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.0564\n",
       "Epoch 8/20\n",
-      "8/8 [==============================] - 0s 2ms/step - loss: 0.0442\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.0504\n",
       "Epoch 9/20\n",
-      "8/8 [==============================] - 0s 3ms/step - loss: 0.0330\n",
+      "8/8 [==============================] - 0s 3ms/step - loss: 0.0365\n",
       "Epoch 10/20\n",
-      "8/8 [==============================] - 0s 3ms/step - loss: 0.0295\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.0352\n",
       "Epoch 11/20\n",
-      "8/8 [==============================] - 0s 3ms/step - loss: 0.0279\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.0328\n",
       "Epoch 12/20\n",
-      "8/8 [==============================] - 0s 3ms/step - loss: 0.0248\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.0282\n",
       "Epoch 13/20\n",
-      "8/8 [==============================] - 0s 3ms/step - loss: 0.0215\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.0249\n",
       "Epoch 14/20\n",
-      "8/8 [==============================] - 0s 4ms/step - loss: 0.0201\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.0258\n",
       "Epoch 15/20\n",
-      "8/8 [==============================] - 0s 3ms/step - loss: 0.0199\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.0215\n",
       "Epoch 16/20\n",
-      "8/8 [==============================] - 0s 3ms/step - loss: 0.0182\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.0202\n",
       "Epoch 17/20\n",
-      "8/8 [==============================] - 0s 2ms/step - loss: 0.0216\n",
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.0199\n",
       "Epoch 18/20\n",
-      "8/8 [==============================] - 0s 3ms/step - loss: 0.0183\n",
+      "8/8 [==============================] - 0s 3ms/step - loss: 0.0181\n",
       "Epoch 19/20\n",
-      "8/8 [==============================] - 0s 2ms/step - loss: 0.0183\n",
+      "8/8 [==============================] - 0s 3ms/step - loss: 0.0188\n",
       "Epoch 20/20\n",
-      "8/8 [==============================] - 0s 2ms/step - loss: 0.0169\n"
+      "8/8 [==============================] - 0s 2ms/step - loss: 0.0167\n"
      ]
     }
    ],
@@ -116,7 +120,7 @@
     "          activations=['relu','relu','relu'],\n",
     "          nepochs=20,batch_size=50,loss='mean_squared_error',\n",
     "          regression='True',nclasses=None,layer_config_file=None,\n",
-    "          opt_config_file=None)\n",
+    "          opt_config=None)\n",
     "\n",
     "mlp.fit(X = X_train, y = y_train)"
    ]
@@ -125,20 +129,71 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Predict the densities for the test data"
+    "### Saving the ChemML model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File saved as ./saved_MLP_chemml_model.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "mlp.save(path=\".\",filename=\"saved_MLP\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading the saved ChemML model "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'chemml.models.keras.mlp.MLP'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "loaded_MLP = MLP()\n",
+    "loaded_MLP = loaded_MLP.load(\"./saved_MLP_chemml_model.csv\")\n",
+    "print(type(loaded_MLP))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Predict the densities for the test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "\n",
-    "y_pred = mlp.predict(X_test)\n",
-    "y_pred = yscale.inverse_transform(y_pred)\n",
-    "y_test = list(y_test[\"density_Kg/m3\"])"
+    "y_pred = loaded_MLP.predict(X_test)\n",
+    "y_pred = yscale.inverse_transform(y_pred.reshape(-1,1))"
    ]
   },
   {
@@ -150,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {
     "scrolled": true
    },
@@ -161,20 +216,8 @@
      "text": [
       "Metrics: \n",
       "\n",
-      "ME:-0.9611486328125002\n",
-      "MAE:11.846704843750016\n",
-      "MSE:240.98763901218734\n",
-      "RMSE:15.523776570544532\n",
-      "MSLE:0.00014722689551004294\n",
-      "RMSLE:0.012133709058241134\n",
-      "MAPE:0.93270660923997\n",
-      "MaxAPE:3.171578239092581\n",
-      "RMSPE:1.218178538988772\n",
-      "MPE:-0.08593577649428302\n",
-      "MaxAE:40.99854003906239\n",
-      "deltaMaxE:81.63212890624982\n",
-      "r_squared:0.9747960374168617\n",
-      "std:97.78291320800781\n"
+      "         MAE      RMSE      MAPE  r_squared\n",
+      "0  11.354159  15.53072  0.886087    0.97493\n"
      ]
     }
    ],
@@ -182,11 +225,9 @@
     "from chemml.utils import regression_metrics\n",
     "\n",
     "# For the regression_metrics function the inputs must have the same data type\n",
-    "\n",
-    "metrics_dict = regression_metrics(y_test, list(y_pred))\n",
+    "metrics_df = regression_metrics(y_test, y_pred)\n",
     "print(\"Metrics: \\n\")\n",
-    "for k in list(metrics_dict.keys())[4:]:\n",
-    "    print(\"{}:{}\".format(k,metrics_dict[k]))"
+    "print(metrics_df[[\"MAE\", \"RMSE\", \"MAPE\", \"r_squared\"]])"
    ]
   },
   {
@@ -198,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -210,7 +251,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -226,8 +267,8 @@
     "import pandas as pd\n",
     "%matplotlib inline\n",
     "df = pd.DataFrame()\n",
-    "df[\"Actual\"] = y_test\n",
-    "df[\"Predicted\"] = list(y_pred)\n",
+    "df[\"Actual\"] = y_test.reshape(-1,)\n",
+    "df[\"Predicted\"] = y_pred.reshape(-1,)\n",
     "sc = scatter2D('r', marker='.')\n",
     "fig = sc.plot(dfx=df, dfy=df, x=\"Actual\", y=\"Predicted\")\n",
     "\n",
@@ -241,13 +282,107 @@
     "sa.save(obj=fig)\n",
     "fig.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### If the keras model is required ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "keras_mlp = mlp.get_keras_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'keras.engine.sequential.Sequential'>\n",
+      "Model: \"sequential_1\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense_4 (Dense)              (None, 32)                6432      \n",
+      "_________________________________________________________________\n",
+      "dense_5 (Dense)              (None, 64)                2112      \n",
+      "_________________________________________________________________\n",
+      "dense_6 (Dense)              (None, 128)               8320      \n",
+      "_________________________________________________________________\n",
+      "dense_7 (Dense)              (None, 1)                 129       \n",
+      "=================================================================\n",
+      "Total params: 16,993\n",
+      "Trainable params: 16,993\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(keras_mlp))\n",
+    "print(keras_mlp.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sometimes you may need the keras model without the output layer (for e.g., for transfer learning)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'keras.engine.functional.Functional'>\n",
+      "Model: \"model\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense_4_input (InputLayer)   [(None, 200)]             0         \n",
+      "_________________________________________________________________\n",
+      "dense_4 (Dense)              (None, 32)                6432      \n",
+      "_________________________________________________________________\n",
+      "dense_5 (Dense)              (None, 64)                2112      \n",
+      "_________________________________________________________________\n",
+      "dense_6 (Dense)              (None, 128)               8320      \n",
+      "=================================================================\n",
+      "Total params: 16,864\n",
+      "Trainable params: 16,864\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "keras_mlp_no_output = mlp.get_keras_model(include_output=False)\n",
+    "print(type(keras_mlp_no_output))\n",
+    "print(keras_mlp_no_output.summary())"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:v2_0.7]",
+   "display_name": "Python [conda env:chemml_env] *",
    "language": "python",
-   "name": "conda-env-v2_0.7-py"
+   "name": "conda-env-chemml_env-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -259,7 +394,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.8.12"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
@@ -10618,8 +10753,7 @@
       "model_module_version": "1.5.0",
       "model_name": "ImageModel",
       "state": {
-       "layout": "IPY_MODEL_0962e110233043bd9ee3d5c81025a25c",
-       "value": {}
+       "layout": "IPY_MODEL_0962e110233043bd9ee3d5c81025a25c"
       }
      },
      "3568e657b7194429853bb50c99266e2f": {
@@ -44405,8 +44539,7 @@
       "model_module_version": "1.5.0",
       "model_name": "ImageModel",
       "state": {
-       "layout": "IPY_MODEL_f459a5bd666d4625b01a8a4c7bf4f34d",
-       "value": {}
+       "layout": "IPY_MODEL_f459a5bd666d4625b01a8a4c7bf4f34d"
       }
      },
      "e4370c2548cf4d23bdbcb7e96514583e": {