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: CalcFunGrad.m
    path of file: ./repos/StackExchangeCodes/Mathematics/Q3307741
    
    the code of the file until where you have to start completion: function [ vG ] = CalcFunGrad( vX, hObjFun, diffMode, epsVal )
% ----------------------------------------------------------------------------------------------- %
% [ vG ] = CalcFunGrad( vX, hObjFun, difMode, epsVal )
%   Calculating the Gradient Vector of a function using Finite Differences
%   method.
% Input:
%   - vX            -   Input Vector.
%                       The point the gradient is caclaulated at.
%                       Structure: Vector (Column).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
%   - hObjFun       -   Objective Function.
%                       Function handler which evaluates the Objective
%                       Function at a given point - hObjFun(vX).
%                       Structure: Function Handler.
%                       Type: Function Handler.
%                       Range: NA.
%   - diffMode      -   Difference Mode.
%                       Sets the mode of operation of the Finite
%                       Differences Method - Forward, Backward, Central or
%                       Complex.
%                       Structure: Scalar.
%                       Type: 'Single' / 'Double'.
%                       Range: {1, 2, 3, 4}.
% Output:
%   - vG            -   Gradient Vector.
%                       The numerical approximation of the gradient of the
%                       Objective Function at the input point 'vX'.
%                       Structure: Vector (Column).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
% References
%   1.  https://en.wikipedia.org/wiki/Finite_difference_coefficient.
%   2.  Nick Higham Talk (https://www.youtube.com/watch?v=Q9OLOqEhc64) At 02:15.
%   3.  Complex Step Differentiation - https://blogs.mathworks.com/cleve/2013/10/14/complex-step-differentiation/.
%   4.  Complex Step Derivative - https://timvieira.github.io/blog/post/2014/08/07/complex-step-derivative/.
%   5.  The Complex Step Derivative Approximation - https://dl.acm.org/citation.cfm?id=838251.
% Remarks:
%   1.  If the Complex Mode is selected the function must return complex
%       values in order to work. For instance, if the input function is
%       'norm(vX)' use 'sum(vX .^ 2)' and if the input function is
%       'sum(abs(vX))' use 'sum(sqrt(vX .^ 2))'.
% TODO:
%   1.  U.
% Release Notes:
%   -   1.1.001     24/06/2019  Royi Avital
%       *   Changed 'difMode' -> 'diffMode'.
%   -   1.1.000     22/08/2018  Royi Avital
%       *   Added Complex Mode.
%   -   1.0.001     13/06/2018  Royi Avital
%       *   Small fixes for better readability.
%   -   1.0.000     08/07/2017  Royi Avital
%       *   First release version.
% ----------------------------------------------------------------------------------------------- %

FALSE   = 0;
TRUE    = 1;

OFF     = 0;
ON      = 1;

DIFF_MODE_FORWARD   = 1;
DIFF_MODE_BACKWARD  = 2;
DIFF_MODE_CENTRAL   = 3;
DIFF_MODE_COMPLEX   = 4;

    
function [ vG ] = CalcFunGrad( vX, hObjFun, diffMode, epsVal )
% ----------------------------------------------------------------------------------------------- %
% [ vG ] = CalcFunGrad( vX, hObjFun, difMode, epsVal )
%   Calculating the Gradient Vector of a function using Finite Differences
%   method.
% Input:
%   - vX            -   Input Vector.
%                       The point the gradient is caclaulated at.
%                       Structure: Vector (Column).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
%   - hObjFun       -   Objective Function.
%                       Function handler which evaluates the Objective
%                       Function at a given point - hObjFun(vX).
%                       Structure: Function Handler.
%                       Type: Function Handler.
%                       Range: NA.
%   - diffMode      -   Difference Mode.
%                       Sets the mode of operation of the Finite
%                       Differences Method - Forward, Backward, Central or
%                       Complex.
%                       Structure: Scalar.
%                       Type: 'Single' / 'Double'.
%                       Range: {1, 2, 3, 4}.
% Output:
%   - vG            -   Gradient Vector.
%                       The numerical approximation of the gradient of the
%                       Objective Function at the input point 'vX'.
%                       Structure: Vector (Column).
%                       Type: 'Single' / 'Double'.
%                       Range: (-inf, inf).
% References
%   1.  https://en.wikipedia.org/wiki/Finite_difference_coefficient.
%   2.  Nick Higham Talk (https://www.youtube.com/watch?v=Q9OLOqEhc64) At 02:15.
%   3.  Complex Step Differentiation - https://blogs.mathworks.com/cleve/2013/10/14/complex-step-differentiation/.
%   4.  Complex Step Derivative - https://timvieira.github.io/blog/post/2014/08/07/complex-step-derivative/.
%   5.  The Complex Step Derivative Approximation - https://dl.acm.org/citation.cfm?id=838251.
% Remarks:
%   1.  If the Complex Mode is selected the function must return complex
%       values in order to work. For instance, if the input function is
%       'norm(vX)' use 'sum(vX .^ 2)' and if the input function is
%       'sum(abs(vX))' use 'sum(sqrt(vX .^ 2))'.
% TODO:
%   1.  U.
% Release Notes:
%   -   1.1.001     24/06/2019  Royi Avital
%       *   Changed 'difMode' -> 'diffMode'.
%   -   1.1.000     22/08/2018  Royi Avital
%       *   Added Complex Mode.
%   -   1.0.001     13/06/2018  Royi Avital
%       *   Small fixes for better readability.
%   -   1.0.000     08/07/2017  Royi Avital
%       *   First release version.
% ----------------------------------------------------------------------------------------------- %

FALSE   = 0;
TRUE    = 1;

OFF     = 0;
ON      = 1;

DIFF_MODE_FORWARD   = 1;
DIFF_MODE_BACKWARD  = 2;
DIFF_MODE_CENTRAL   = 3;
DIFF_MODE_COMPLEX   = 4;


numElements = size(vX, 1);

objFunRef   = hObjFun(vX);
vG          = zeros([numElements, 1]);
vPertVal    = zeros([numElements, 1]);

switch(diffMode)
    case(DIFF_MODE_FORWARD)
        hCalcGradFun = @(vPertVal) (hObjFun(vX + vPertVal) - objFunRef) / epsVal;
    case(DIFF_MODE_BACKWARD)
        hCalcGradFun = @(vPertVal) (objFunRef - hObjFun(vX - vPertVal)) / epsVal;
    case(DIFF_MODE_CENTRAL)
        hCalcGradFun = @(vPertVal) (hObjFun(vX + vPertVal) - hObjFun(vX - vPertVal)) / (2 * epsVal);
    case(DIFF_MODE_COMPLEX)
        hCalcGradFun = @(vPertVal) imag(hObjFun(vX + (1i * vPertVal))) / epsVal;
end