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: mhmmParzen_train_observed.m
    path of file: ./repos/bnt/HMM
    
    the code of the file until where you have to start completion: function [initState, transmat, mu, Nproto, pick] = mhmmParzen_train_observed(obsData, hiddenData, ...
						  nstates, maxNproto, varargin)
% mhmmParzentrain_observed  with mixture of Gaussian outputs from fully observed sequences
% function [initState, transmat, mu, Nproto] = mhmm_train_observed_parzen(obsData, hiddenData, ...
%						  nstates, maxNproto)
%
%
% INPUT
% If all sequences have the same length

    
function [initState, transmat, mu, Nproto, pick] = mhmmParzen_train_observed(obsData, hiddenData, ...
						  nstates, maxNproto, varargin)
% mhmmParzentrain_observed  with mixture of Gaussian outputs from fully observed sequences
% function [initState, transmat, mu, Nproto] = mhmm_train_observed_parzen(obsData, hiddenData, ...
%						  nstates, maxNproto)
%
%
% INPUT
% If all sequences have the same length
% obsData(:,t,ex) 
% hiddenData(ex,t)  - must be ROW vector if only one sequence
% If sequences have different lengths, we use cell arrays
% obsData{ex}(:,t) 
% hiddenData{ex}(t)
%
% Optional argumnets
% dirichletPriorWeight - for smoothing transition matrix counts
% mkSymmetric
%
% Output
% mu(:,q)
% Nproto(q) is the number of prototypes (mixture components) chosen for state q

[transmat, initState] = transmat_train_observed(...
    hiddenData, nstates, varargin{:});

% convert to obsData(:,t*nex)
if ~iscell(obsData)
  [D T Nex] = size(obsData);
  obsData = reshape(obsData, D, T*Nex);
else
  obsData = cat(2, obsData{:});
  hiddenData = cat(2, hiddenData{:});
end