You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: get_ann_manager.m
    path of file: ./repos/AI-mag/src/ann_regression/ann_example/ann_data
    
    the code of the file until where you have to start completion: function get_ann_manager(ann_type)
% Function for testing the 'AnnManager' class in a systematic way.
%
%    Parameters:
%        ann_type (str): type of the regression method to be used
%
%    (c) 2019-2020, ETH Zurich, Power Electronic Systems Laboratory, T. Guillod

% init
fprintf('################## master_train : %s\n', ann_type)

% get the data
fprintf('get data\n')
ann_input = get_ann_param(ann_type);
[n_sol, inp, out_ref, out_nrm] = get_ann_data();

% create a AnnManager instance
fprintf('create object\n')
obj = AnnManager(ann_input);

% train the ANN or fit the regression
fprintf('train and fit\n')
obj.train(n_sol, inp, out_ref, out_nrm);

% get the figures of merit of the regression
fprintf('get figures of merit\n')
fom = obj.get_fom();
assert(isstruct(fom), 'invalid fom data type')

% display and plot the figures of merit of the regression
fprintf('display information\n')
obj.disp();

% dump all the data contained in the object
fprintf('dump\n')
[ann_input, ann_data] = obj.dump();

% delete the object
fprintf('delete\n')
obj.delete();

% used the dump data to reconstruct the object and evaluate the data

    
function get_ann_manager(ann_type)
% Function for testing the 'AnnManager' class in a systematic way.
%
%    Parameters:
%        ann_type (str): type of the regression method to be used
%
%    (c) 2019-2020, ETH Zurich, Power Electronic Systems Laboratory, T. Guillod

% init
fprintf('################## master_train : %s\n', ann_type)

% get the data
fprintf('get data\n')
ann_input = get_ann_param(ann_type);
[n_sol, inp, out_ref, out_nrm] = get_ann_data();

% create a AnnManager instance
fprintf('create object\n')
obj = AnnManager(ann_input);

% train the ANN or fit the regression
fprintf('train and fit\n')
obj.train(n_sol, inp, out_ref, out_nrm);

% get the figures of merit of the regression
fprintf('get figures of merit\n')
fom = obj.get_fom();
assert(isstruct(fom), 'invalid fom data type')

% display and plot the figures of merit of the regression
fprintf('display information\n')
obj.disp();

% dump all the data contained in the object
fprintf('dump\n')
[ann_input, ann_data] = obj.dump();

% delete the object
fprintf('delete\n')
obj.delete();

% used the dump data to reconstruct the object and evaluate the data
fprintf('predict\n')
predict(ann_input, ann_data)

% teardown
fprintf('################## master_train : %s\n', ann_type)

end