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: trend_documentation.m
    path of file: ./repos/CDT/cdt/doc
    
    the code of the file until where you have to start completion: %% |trend| documentation
% |trend| calculates the linear trend of a data series by least squares. Data
% do not need to be equally spaced in time. 
% 
% See also <detrend3_documentation.html |detrend3|>.
%
% <CDT_Contents.html Back to Climate Data Tools Contents>.
%% Syntax
% 
%  tr = trend(y) 
%  tr = trend(y,Fs) 
%  tr = trend(y,t) 
%  tr = trend(...,'dim',dim)
%  tr = trend(...,'omitnan')
%  [tr,p] = trend(...)
%  [tr,p] = trend(...,corrOptions)
% 
%% Description
% 
% |tr = trend(y)| calculates the linear trend per sample of |y|. 
% 
% |tr = trend(y,Fs)| specifies a sampling rate |Fs|. For example, to obtain a trend
% per year from data collected at monthly resolution, set |Fs| equal to 12. This
% syntax assumes all values in y are equally spaced in time. 
% 
% |tr = trend(y,t)| specifies a vector |t| relative to which the trend is calculated. 
% Each element of t corresponds to a measurement in |
    
function gives the trend in |y| per
% sample. Specify the sampling rate of 12 samples per year like this: 

trend(y,Fs) 

%% 
% ...and that's the value close to 40 that we were expecting. It's not exactly
% 40 because we intentionally added noise, but it's about 40, as expected. 
% 
% You may have noticed by now that the trend can be calculated any of three 
% ways, and each will give the same answer. You can multiply the trend by 
% the sampling rate, or you can specify the sampling rate in the |trend| function, 
% or you can specify the corresponding time vector |t|, and they'll all give 
% the same answer: 

[trend(y)*Fs trend(y,Fs) trend(y,t)]

%% Example 2: 2D data
% If you have a bunch of measurements that all line up to the same times, 
% the |trend| function can calculate all the trends at once, in a computationally
% efficient manner. Consider these four time series built from the |y| array
% we defined in example 1: 

%   [y    y+400        very noisy y           only noise     ];
A = [y    y+400     y+200*randn(size(y))    100*rand(size(y))];

figure
plot(t,A)
legend('column 1','column 2','column 3','column 4',...
   'location','northwest') 
xlabel 'time'
ylabel 'some variable'
axis tight
box off       % removes ugly frame from axes
legend boxoff % removes ugly frame from legend

%%
% The trends of each column can be computed like this: 

trend(A,t)

%%
% That's what we expected: About 40 units/year for columns 1 and 2, which are
% the same data aside from the offset; somewhat of a trend for column 3, and 
% not much of a trend for the noise-only column 4. 
% 
% *Are any of these trends statistically significant?* Use the optional 
% second function output to see: 

[tr,p] = trend(A,t) 

%%
% The p values for the first three columns indicate statistical significance, 
% but the fourth column tells us that any trend