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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 addRandomEffect(self, K=None, is_noise=False, normalize=False, Kcross=None, trait_covar_type='freeform', rank=1, fixed_trait_covar=None, jitter=1e-4):
 """ Add random effects term. Args: K: Sample Covariance Matrix [N, N] is_noise: Boolean indicator specifying if the matrix is homoscedastic noise (weighted identity covariance) (default False) normalize: Boolean indicator specifying if K has to be normalized such that K.trace()=N. Kcross: NxNtest cross covariance for predictions trait_covar_type: type of covaraince to use. Default 'freeform'. possible values are 'freeform': general semi-definite positive matrix, 'fixed': fixed matrix specified in fixed_trait_covar, 'diag': diagonal matrix, 'lowrank': low rank matrix. The rank of the lowrank part is specified in the variable rank, 'lowrank_id': sum of a lowrank matrix and a multiple of the identity. The rank of the lowrank part is specified in the variable rank, 'lowrank_diag': sum of a lowrank and a diagonal matrix. The rank of the lowrank part is specified in the variable rank, 'block': multiple of a matrix of ones, 'block_id': sum of a multiple of a matrix of ones and a multiple of the idenity, 'block_diag': sum of a multiple of a matrix of ones and a diagonal matrix, rank: rank of a possible lowrank component (default 1) fixed_trait_covar: PxP matrix for the (predefined) trait-to-trait covariance matrix if fixed type is used jitter: diagonal contribution added to trait-to-trait covariance matrices for regularization """

        assert K is not None or is_noise, 'VarianceDecomposition:: Specify covariance structure'

        if is_noise:
            assert self.noisPos is None, 'VarianceDecomposition:: Noise term already exists'
            K  = sp.eye(self.N)
            Kcross = None
            self.noisPos = self.n_randEffs
        else:
            assert K.shape[0]==self.N, 'VarianceDecomposition:: Incompatible shape for K'
            assert K.shape[1]==self.N, 'VarianceDecomposition:: Incompatible shape for K'
        if Kcross is not None:
            assert self.Ntest is not None, 'VarianceDecomposition:: specify Ntest for predictions (method VarianceDecomposition::setTestSampleSize)'
            assert Kcross.shape[0]==self.N, 'VarianceDecomposition:: Incompatible shape for Kcross'
            assert Kcross.shape[1]==self.Ntest, 'VarianceDecomposition:: Incompatible shape for Kcross'

        if normalize:
            cc = covar_rescaling_factor(K) 
            K *= cc
            if Kcross is not None: Kcross *= cc 

        # add random effect
        self.sample_covars.append(K)
        self.sample_cross_covars.append(Kcross)
        _cov = self._buildTraitCovar(trait_covar_type=trait_covar_type, rank=rank, fixed_trait_covar=fixed_trait_covar, jitter=jitter)
        self.trait_covars.append(_cov)
        self.n_randEffs+=1

        #TODO: define _sync function
        self._desync()