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: master_compute_pareto.m
    path of file: ./repos/AI-mag/src/inductor_design
    
    the code of the file until where you have to start completion: function master_compute_pareto(file_compute_all, file_export, sweep, n_split, fct, eval_ann, data_compute)
% Compute many inductor inductor designs (properties, thermal, losses, etc.).
%
%    Load the ANN/regression obtained with the FEM/ANN workflow.
%    Sweep different inductor designs.
%    Compute the inductor properties, thermal, losses, etc.
%
%    Use the ANN/regression are used for predicting:
%        - the thermal model (hotspot and average temperatures)
%        - the magnetic model (inductance, current density, flux density, and magnetic field)
%
%    The complete code is running:
%        - parallel: on different MATLAB workers
%        - vectorized: many designs at the same time
%
%    Warning: The code use functions that are internally (multithreaded).
%             Therefore, the speedup achieved with parallel MATLAB workers can be very low.
%
%    This function requires a running ANN Python Server (if this regression method is used).
%
%    Parameters:
%        file_compute_all (str): path of the file to be written with the computed designs
%        file_export (str): path of the file containing the exported data from the FEM/ANN
%        sweep (cell): data controlling the generation of the design combinations
%        n_split (int): number of vectorized designs per computation
%        fct (struct): struct with custom functions for filtering invalid designs
%        eval_ann (struct): data for controlling the evaluation of the ANN/regression
%        data_compute (struct): data for the inductor designs
%
%    (c) 2019-2020, ETH Zurich, Power Electronic Systems Laboratory, T. Guillod

% init
fprintf('################## master_compute_pareto\n')

% load the FEM/ANN data
fprintf('load\n')
data_ann_fem = load(file_export);

% time start
tic = datetime('now');

% create the object for evaluating the geometry, thermal, and loss models with ANN/regression
fprintf('ann fem\n')
ann_fem_obj = AnnFem(data_ann_fem, eval_ann.geom_type, eval_ann.eval_type);

% generate the combinations to be computed
fprintf('sweep\n')
[n_tot, var] = get_sweep_combine(sweep);

% split the designs into chunks for parallel computing
fprintf('split\n')
[n_chunk, idx_chunk] = get_chunk(n_split, n_tot);


    
function master_compute_pareto(file_compute_all, file_export, sweep, n_split, fct, eval_ann, data_compute)
% Compute many inductor inductor designs (properties, thermal, losses, etc.).
%
%    Load the ANN/regression obtained with the FEM/ANN workflow.
%    Sweep different inductor designs.
%    Compute the inductor properties, thermal, losses, etc.
%
%    Use the ANN/regression are used for predicting:
%        - the thermal model (hotspot and average temperatures)
%        - the magnetic model (inductance, current density, flux density, and magnetic field)
%
%    The complete code is running:
%        - parallel: on different MATLAB workers
%        - vectorized: many designs at the same time
%
%    Warning: The code use functions that are internally (multithreaded).
%             Therefore, the speedup achieved with parallel MATLAB workers can be very low.
%
%    This function requires a running ANN Python Server (if this regression method is used).
%
%    Parameters:
%        file_compute_all (str): path of the file to be written with the computed designs
%        file_export (str): path of the file containing the exported data from the FEM/ANN
%        sweep (cell): data controlling the generation of the design combinations
%        n_split (int): number of vectorized designs per computation
%        fct (struct): struct with custom functions for filtering invalid designs
%        eval_ann (struct): data for controlling the evaluation of the ANN/regression
%        data_compute (struct): data for the inductor designs
%
%    (c) 2019-2020, ETH Zurich, Power Electronic Systems Laboratory, T. Guillod

% init
fprintf('################## master_compute_pareto\n')

% load the FEM/ANN data
fprintf('load\n')
data_ann_fem = load(file_export);

% time start
tic = datetime('now');

% create the object for evaluating the geometry, thermal, and loss models with ANN/regression
fprintf('ann fem\n')
ann_fem_obj = AnnFem(data_ann_fem, eval_ann.geom_type, eval_ann.eval_type);

% generate the combinations to be computed
fprintf('sweep\n')
[n_tot, var] = get_sweep_combine(sweep);

% split the designs into chunks for parallel computing
fprintf('split\n')
[n_chunk, idx_chunk] = get_chunk(n_split, n_tot);

% compute the designs in parallel
fprintf('run\n')
parfor i=1:n_chunk
    fprintf('    %d / %d\n', i, n_chunk)
    [track(i), fom(i), operating(i)] = compute_chunk(var, idx_chunk{i}, fct, ann_fem_obj, data_compute);
end