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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def getCovariance(self,normalize=True,i0=None,i1=None,pos0=None,pos1=None,chrom=None,center=True,unit=True,pos_cum0=None,pos_cum1=None,blocksize=None,X=None,**kw_args):
 """calculate the empirical genotype covariance in a region"""

        if X is not None:
            K=X.dot(X.T)
            Nsnp=X.shape[1]
        else:
            if (i0 is None) and (i1 is None) and ((pos0 is not None) & (pos1 is not None) & (chrom is not None)) or ((pos_cum0 is not None) & (pos_cum1 is not None)):
                i0,i1=self.getGenoIndex(pos0=pos0,pos1=pos1,chrom=chrom,pos_cum0=pos_cum0,pos_cum1=pose_cum1)

            [N,M]=self.genoM.shape
            if blocksize is None:
                blocksize=M
            if i0 is None:
                i0=0
            if i1 is None:
                i1=M
            nread = i0
            K=None
            Nsnp=i1-i0
            while nread<i1:
                thisblock=min(blocksize,i1-nread)
                X=self.getGenotypes(i0=nread,i1=(nread+thisblock),center=center,unit=unit,**kw_args)    
                if K is None:
                    K=X.dot(X.T)
                else:
                    K+=X.dot(X.T)
                nread+=thisblock
        if normalize:
            K/=(K.diagonal().mean())
        else:#divide by number of SNPs in K
            K/=Nsnp
        return K