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: pdist_plot.m
    path of file: ./repos/CanlabCore/CanlabCore/External/umap/umap
    
    the code of the file until where you have to start completion: function [corr, embedding] = pdist_plot(csv_file_or_data, varargin)
%PDIST_PLOT Given a high-dimensional dataset, pdist_plot produces a density plot
% comparing pairwise distance between sample points in high dimension to
% pairwise distance between the points in a low-dimensional representation
% produced by UMAP.
%
% Ideally, one will see a high correlation between these two variables.
%
% corr = pdist_plot(csv_file_or_data)
%
% Required input
% ----------
% csv_file_or_data:
%   This is either 
%   A) a char array identifying a CSV text file containing the data 
%      to be reduced. 
%   B) the actual data to be reduced; a numeric matrix.
%
% Optional input
% ----------
% 'template_file':
%     identifies a .mat file with a saved instance of the UMAP class that
%     run_umap previously produced.
%
% 'label_column': integer
%     identifies the column in the input data matrix which contains numeric
%     identifiers to label the data for UMAP supervision mode.
%
% 'sample_size': integer
%     number of sample points to use to create the pairwise distance plot.
%     Because the computation time is O(n^2) in this input, the maximum
%     accepted value is 10,000.
% 
% 'verbose': char array
%     controls verbosity of output in the same manner as the argument for
%     run_umap.m.
%
% 'vary_dims': boolean
%     if true, pdist_plot will repeat itself by runing UMAP to produce a
%     d-dimensional embedding for all d between 2 and the dimensionality of
%     the original data.
%
% Returns
% -------
% corr: double
%     The correlation coefficient of the two pairwise distance variables:
%     high dimension and low dimension.
%
%   AUTHORSHIP
%   Math Lead & Primary Developer:  Connor Meehan <connor.gw.meehan@gmail.com>
%   Secondary Developer: Stephen Meehan <swmeehan@stanford.edu>
%   Bioinformatics Lead:  Wayne Moore <wmoore@stanford.edu>
%   Provided by the Herzenberg Lab at Stanford University 
%   License: BSD 3 clause
%

MAX_DIMENSIONS = 50;

p=parseArguments();
parse(p,varargin{:});
args=p.Results;
sample_size = args.sample_size;
label_column = args.label_column;
template_file = args.template_file;
verbose = args.verbose;
vary_dims = args.vary_dims;
supervised = label_column > 0;
template_applied = ~isempty(template_file);
if ischar(csv_file_or_data)
    csv_file_or_data=UmapUtil.RelocateExamples(csv_file_or_data);
    t=readtable(csv_file_or_data, 'ReadVariableNames', true);

    
function [corr, embedding] = pdist_plot(csv_file_or_data, varargin)
%PDIST_PLOT Given a high-dimensional dataset, pdist_plot produces a density plot
% comparing pairwise distance between sample points in high dimension to
% pairwise distance between the points in a low-dimensional representation
% produced by UMAP.
%
% Ideally, one will see a high correlation between these two variables.
%
% corr = pdist_plot(csv_file_or_data)
%
% Required input
% ----------
% csv_file_or_data:
%   This is either 
%   A) a char array identifying a CSV text file containing the data 
%      to be reduced. 
%   B) the actual data to be reduced; a numeric matrix.
%
% Optional input
% ----------
% 'template_file':
%     identifies a .mat file with a saved instance of the UMAP class that
%     run_umap previously produced.
%
% 'label_column': integer
%     identifies the column in the input data matrix which contains numeric
%     identifiers to label the data for UMAP supervision mode.
%
% 'sample_size': integer
%     number of sample points to use to create the pairwise distance plot.
%     Because the computation time is O(n^2) in this input, the maximum
%     accepted value is 10,000.
% 
% 'verbose': char array
%     controls verbosity of output in the same manner as the argument for
%     run_umap.m.
%
% 'vary_dims': boolean
%     if true, pdist_plot will repeat itself by runing UMAP to produce a
%     d-dimensional embedding for all d between 2 and the dimensionality of
%     the original data.
%
% Returns
% -------
% corr: double
%     The correlation coefficient of the two pairwise distance variables:
%     high dimension and low dimension.
%
%   AUTHORSHIP
%   Math Lead & Primary Developer:  Connor Meehan <connor.gw.meehan@gmail.com>
%   Secondary Developer: Stephen Meehan <swmeehan@stanford.edu>
%   Bioinformatics Lead:  Wayne Moore <wmoore@stanford.edu>
%   Provided by the Herzenberg Lab at Stanford University 
%   License: BSD 3 clause
%

MAX_DIMENSIONS = 50;

p=parseArguments();
parse(p,varargin{:});
args=p.Results;
sample_size = args.sample_size;
label_column = args.label_column;
template_file = args.template_file;
verbose = args.verbose;
vary_dims = args.vary_dims;
supervised = label_column > 0;
template_applied = ~isempty(template_file);
if ischar(csv_file_or_data)
    csv_file_or_data=UmapUtil.RelocateExamples(csv_file_or_data);
    t=readtable(csv_file_or_data, 'ReadVariableNames', true);
    inData=table2array(t);
else
    inData = csv_file_or_data;
end