def retrieve_hyperparameters(current_model_name: str):
    'Based on the current_model_name returns a list of hyperparameters used for optimizing the model (if necessary).\n\n        Args:\n            current_model_name: Name of the model currently expected to be trained\n\n        Returns:\n            A dictionary containing the hyperparameter name and the values that will be used to optimize the model\n    '
    if (current_model_name == 'support_vector_classifier'):
        parameters = {'kernel': ['linear', 'poly', 'rbf']}
    elif (current_model_name == 'decision_tree_classifier'):
        parameters = {'criterion': ['gini', 'entropy'], 'splitter': ['best', 'random'], 'max_depth': [2, 3, 4, 5, 6, 7]}
    elif ((current_model_name == 'random_forest_classifier') or (current_model_name == 'extra_trees_classifier')):
        parameters = {'n_estimators': [(i * 10) for i in range(2, 11, 2)], 'criterion': ['gini', 'entropy'], 'max_depth': [2, 3, 4, 5, 6, 7]}
    elif (current_model_name == 'gradient_boosting_classifier'):
        parameters = {'max_depth': [2, 3, 4, 5, 6, 7], 'n_estimators': [(i * 10) for i in range(2, 11, 2)]}
    else:
        parameters = {'None': ['None']}
    return parameters
Based on the current_model_name returns a list of hyperparameters used for optimizing the model (if necessary).

Args:
    current_model_name: Name of the model currently expected to be trained

Returns:
    A dictionary containing the hyperparameter name and the values that will be used to optimize the model
def retrieve_hyperparameters(current_model_name: str):
    'Based on the current_model_name returns a list of hyperparameters used for optimizing the model (if necessary).\n\n        Args:\n            current_model_name: Name of the model currently expected to be trained\n\n        Returns:\n            A dictionary containing the hyperparameter name and the values that will be used to optimize the model\n    '
    if (current_model_name == 'support_vector_classifier'):
        parameters = {'kernel': ['linear', 'poly', 'rbf']}
    elif (current_model_name == 'decision_tree_classifier'):
        parameters = {'criterion': ['gini', 'entropy'], 'splitter': ['best', 'random'], 'max_depth': [2, 3, 4, 5, 6, 7]}
    elif ((current_model_name == 'random_forest_classifier') or (current_model_name == 'extra_trees_classifier')):
        parameters = {'n_estimators': [(i * 10) for i in range(2, 11, 2)], 'criterion': ['gini', 'entropy'], 'max_depth': [2, 3, 4, 5, 6, 7]}
    elif (current_model_name == 'gradient_boosting_classifier'):
        parameters = {'max_depth': [2, 3, 4, 5, 6, 7], 'n_estimators': [(i * 10) for i in range(2, 11, 2)]}
    else:
        parameters = {'None': ['None']}
    return parameters