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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 sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, verbose=False, log=False):
 """ Solve the entropic regularization optimal transport problem with group lasso regularization The function solves the following optimization problem: .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma) s.t. \gamma 1 = a \gamma^T 1= b \gamma\geq 0 where : - M is the (ns,nt) metric cost matrix - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - :math:`\Omega_g` is the group lasso regulaization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^2` where :math:`\mathcal{I}_c` are the index of samples from class c in the source domain. - a and b are source and target weights (sum to 1) The algorithm used for solving the problem is the generalised conditional gradient as proposed in [5]_ [7]_ Parameters a : np.ndarray (ns,) samples weights in the source domain labels_a : np.ndarray (ns,) labels of samples in the source domain b : np.ndarray (nt,) samples in the target domain M : np.ndarray (ns,nt) loss matrix reg : float Regularization term for entropic regularization >0 eta : float, optional Regularization term for group lasso regularization >0 numItermax : int, optional Max number of iterations numInnerItermax : int, optional Max number of iterations (inner sinkhorn solver) stopInnerThr : float, optional Stop threshold on error (inner sinkhorn solver) (>0) verbose : bool, optional Print information along iterations log : bool, optional record log if True Returns ------- gamma : (ns x nt) ndarray Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters References .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. See Also -------- ot.optim.gcg : Generalized conditional gradient for OT problems """

    lstlab = np.unique(labels_a)

    def f(G):
        res = 0
        for i in range(G.shape[1]):
            for lab in lstlab:
                temp = G[labels_a == lab, i]
                res += np.linalg.norm(temp)
        return res

    def df(G):
        W = np.zeros(G.shape)
        for i in range(G.shape[1]):
            for lab in lstlab:
                temp = G[labels_a == lab, i]
                n = np.linalg.norm(temp)
                if n:
                    W[labels_a == lab, i] = temp / n
        return W

    return gcg(a, b, M, reg, eta, f, df, G0=None, numItermax=numItermax,
               numInnerItermax=numInnerItermax, stopThr=stopInnerThr,
               verbose=verbose, log=log)